{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "  <img src=\"https://github.com/wisupai/e2m/blob/main/docs/images/wisup_e2m_banner.jpg?raw=true\" width=\"100%\" alt=\"wisup_e2m Logo\">\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 👏🏻欢迎使用E2M(Everything2Markdown)👏🏻\n",
    "\n",
    "## 📖简介\n",
    "\n",
    "E2M是一个将各种内容转换为Markdown格式的工具，支持多种输入格式:\n",
    "\n",
    "-  文本\n",
    "    -   doc\n",
    "    -   docx\n",
    "    -   epub\n",
    "    -   html\n",
    "    -   htm\n",
    "    -   pdf (特别说明:包括纯文本、文本+图片、纯图片形式的pdf文件)\n",
    "    -   ppt\n",
    "    -   pptx\n",
    "-   链接\n",
    "    -   url\n",
    "-   音频\n",
    "    -   mp3\n",
    "    -   m4a\n",
    "- 视频\n",
    "    -  mp4 (未完成)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🚀快速上手\n",
    "\n",
    "### 🔧安装\n",
    "\n",
    "```bash\n",
    "pip install wisup_e2m\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 核心功能1: 解析器\n",
    "\n",
    "解析器（Parser）的目的是将各类文件解析为文本或者图片。因为大模型的输入主要以文本和图片为主，所以解析器是在运行转换器（Converter）前的预处理步骤。\n",
    "\n",
    "解析后返回的数据格式为 E2MParsedData:\n",
    "\n",
    "```python\n",
    "class E2MParsedData(BaseModel):\n",
    "    text: Optional[str] = Field(None, description=\"Parsed text\")\n",
    "    images: Optional[List[str]] = Field([], description=\"Parsed image paths\")\n",
    "    attached_images: Optional[List[str]] = Field(\n",
    "        [], description=\"Attached image paths, like 1_0.png, 1_1.png, etc.\"\n",
    "    )\n",
    "    attached_images_map: Optional[Dict[str, List[str]]] = Field(\n",
    "        {},\n",
    "        description=\"Attached image paths map, like {1.png: ['/path/to/1_0.png'], 2.png: [/path/to/2_1.png]}, only available for layout detection.\",\n",
    "    )\n",
    "    metadata: Optional[List[Any] | Dict[str, Any]] = Field(\n",
    "        {}, description=\"Metadata of the parsed data, including engine, etc.\"\n",
    "    )\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### URL 解析器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wisup_e2m import UrlParser\n",
    "\n",
    "url = \"https://www.osar.fr/notes/justintonation\"\n",
    "parser = UrlParser(engine=\"jina\") # url engines: jina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title: What I Learned Writing an Album in Just Intonation\n",
      "\n",
      "URL Source: https://www.osar.fr/notes/justintonation\n",
      "\n",
      "Markdown Content:\n",
      "Pierre Cusa <pierre@osar.fr>  \n",
      "August 2024 [(history)](https://github.com/pac-dev/notes/commits/master/content/justintonation.md)  \n",
      "in [osar.fr](https://www.osar.fr/) / notes\n",
      "\n",
      "**Just intonation (JI)** today falls under the umbrella of microtonal music, because it involves playing \"between the notes\" of the 12-tone scale we're used to. As I type microtonal, my spellchecker decorates it with a sad squiggle, confirming I've drifted (again) into a topic that's not even in its dictionary. But JI is often described as _the way music should naturally sound_. So which one is it, an obscure deviation from the norm, or an ideal we should return to?\n",
      "\n",
      "This post contains all the notes I want to keep after writing [an album](https://www.cusamusic.com/album/walk/) in JI, specifically using a very uncompromising method called freestyle JI. The album itself isn't the focus here, but it allowed me to get a practical perspective on a topic that's often described more theoretically. This practical perspective is exactly what you'll find here.\n",
      "\n",
      "Contents\n",
      "\n",
      "*   [Basics of Just Intonation](https://www.osar.fr/notes/justintonation#basics-of-just-intonation)\n",
      "*   [The \"Boring\" Solution: Scales](https://www.osar.fr/notes/justintonation#the-boring-solution-scales)\n",
      "*   [The Far-Out Solution: Freestyle JI](https://www.osar.fr/notes/justintonation#the-far-out-solution-freestyle-ji)\n",
      "*   [Building Chords and Composing in Freestyle JI](https://www.osar.fr/notes/justintonation#building-chords-and-composing-in-freestyle-ji)\n",
      "*   [Dissonance in JI: The Devil's Bargain](https://www.osar.fr/notes/justintonation#dissonance-in-ji-the-devils-bargain)\n",
      "*   [Thinking in Different Tunings](https://www.osar.fr/notes/justintonation#thinking-in-different-tunings)\n",
      "*   [Further reading: It's a tone-eat-tone world out there](https://www.osar.fr/notes/justintonation#further-reading-its-a-tone-eat-tone-world-out-there)\n",
      "\n",
      "Basics of Just Intonation\n",
      "-------------------------\n",
      "\n",
      "In order to understand JI, first you have to \"empty your cup\". Most people's cup is occupied by **12-tone equal temperament (12TET)**, which defines the notes we're typically allowed to play. To understand 12TET, start with the octave: going up or down one octave means multiplying or dividing the frequency by 2. Then, divide this octave into 12 equally spaced semitones. This \"space between semitones\" is actually a specific irrational number: 𝒔 \\=212\\=\\\\sqrt\\[12\\]{2}. Multiplying by this number 12 times is equivalent to a single multiplication by 2 (the octave):\n",
      "\n",
      "12-tone equal temperament. Circles contain approximate frequencies in Hertz.  \n",
      "_𝒔_ \\=212≈1.0595...\\=\\\\sqrt\\[12\\]{2} \\\\approx 1.0595...\n",
      "\n",
      "Just intonation is a broader system which does not equally divide the octave in any way. In JI, you take your reference pitch, then multiply or divide its frequency by any whole number to get playable tones as desired. In other words, all intervals in JI are natural ratios, while in 12TET, this is only the case for the octave.\n",
      "\n",
      "Example of JI intervals. Circles contain approximate frequencies in Hertz.\n",
      "\n",
      "In psychoacoustics, it's generally accepted that our perception of harmony relies on the interpretation of intervals as natural ratios. Simplifying a bit, this means JI is an exact representation of the way we perceive harmony, while 12TET gets harmonic significance from the fact that **it contains intervals that are good approximations of JI intervals**:\n",
      "\n",
      "A simple melody in both JI and 12TET.\n",
      "\n",
      "The theory, so far, tells us that JI should be more consonant than 12TET, but the above examples don't show this convincingly. This is where \"writing an album\" comes in. To me, making music is the goal of researching it, but it also guides and informs the research, giving a means to judge subjectively whether the results are useful. Below, we'll get to deeper theory, with examples of things that worked noticeably better in JI, but also things that fundamentally don't work in JI.\n",
      "\n",
      "But before composing in JI, it's necessary to have tools that allow it, and this is more challenging than it might seem. Even simple JI intervals inside a single octave can actually result in an infinity of possible tones, so it seems like we should somehow narrow down the possibilities.\n",
      "\n",
      "The \"Boring\" Solution: Scales\n",
      "-----------------------------\n",
      "\n",
      "Music-making tools, both digital and physical, often assume you want to work with a limited set of predefined tones. This clashes with \"raw\" JI and its infinite level of detail. The most common solution, both ancient and modern, is to use a subset of JI with 12 tones per octave. Assuming a modern mindset, this effectively turns JI into an alternative tuning for a familiar musical system.\n",
      "\n",
      "Example of a 12-tone tuning based on JI: the Pythagorean tuning.  \n",
      "Circles contain approximate frequencies in Hertz.\n",
      "\n",
      "I won't dwell too long on the creation of just-intoned, general-purpose scales, but it's important to understand that this approach is central to the history of music[1](https://www.osar.fr/notes/justintonation#fn:1). There are still some situations where it might make sense to use such scales, but I'll venture to say that in the common case, it's not a great idea. The results easily end up more dissonant than 12TET when the wrong intervals are played, and it's surprisingly hard to avoid the \"wrong intervals\", something I'll detail further down.\n",
      "\n",
      "A more creative approach is to experiment with alternative sets of tones that don't attempt to mimic 12TET. These might not have a fixed number of tones per octave, and are often laid out with more consonant tones closer together, which takes some of the edge off the problem mentioned above. These alternative layouts are especially useful to bring physical instruments closer to just intonation.\n",
      "\n",
      "![Image 1](![ji_instruments.jpg?version=1723776197](figures/ji_instruments.jpg?version=1723776197))\n",
      "\n",
      "The Far-Out Solution: Freestyle JI\n",
      "----------------------------------\n",
      "\n",
      "So far, we've bent to the common view that JI can't be used directly, and that you need to narrow it down to a chosen scale or tuning. But what if we don't want to use a predefined set of tones at all? This is what I've been calling \"raw\" JI, and it's actually an existing approach called freestyle JI, which means you have to be ready to give each note its individual frequency, with all frequencies being natural ratios of each other. This also has the reputation of being terribly impractical, which is always a selling point for me.\n",
      "\n",
      "Let's see how freestyle JI can make an interesting composition. Imagine the composition moves through a few chords. In a system of fixed tones, you would look at your context (the current chord, maybe the overall key, etc.) and pick the best-matching tones from your predefined set. My approach to freestyle JI was to **redefine which tones are available** whenever the context changes, or in fact, at arbitrary moments.\n",
      "\n",
      "Here's an example of two successive chords which have their \"bespoke\" tones defined individually:\n",
      "\n",
      "In the example above, you can compare the same chords in JI and in their closest possible 12TET approximation[2](https://www.osar.fr/notes/justintonation#fn:2). I'll just say that our habituation to 12TET makes things difficult to judge, but to me, a listening test reveals two things that work better in JI: first, \"chord 2\" is noticeably more consonant and understandable in JI. This is partly because it contains more [7-limit](https://en.xen.wiki/w/7-limit) intervals, which are more difficult to reproduce in 12TET. Second, the transition between the two chords flows better in JI than in 12TET. I attribute this to keeping shared tones equal, but constructing other tones directly from the shared ones.\n",
      "\n",
      "This brings us to the variety of tones _between_ chords, which becomes more obvious when composing. Throwing all the tones into a single tuning or scale would hardly make sense, with tiny sub-semitone intervals like the one between `9/5` in the first chord and `15/8` in the second. More awkward, arbitrary intervals would appear as more chords are added.\n",
      "\n",
      "By avoiding any global scale or tuning, tones between chords are allowed to become unrelated, and tend to become even more unrelated between non-adjacent chords. This is exactly what happens throughout the album in question: the harmony is consonant and continuous, but if you take a global view, the album actually contains over 40 distinct tones in a single octave. Could I have built a 40-tone scale and used it to compose the whole thing? Not really: there would be no way to predict which ones are required in advance!\n",
      "\n",
      "Building Chords and Composing in Freestyle JI\n",
      "---------------------------------------------\n",
      "\n",
      "This is not a guide on composing in JI, the objective is more to understand the value of JI and to see where it fits. That said, it would be rich to write all this without even giving a hint of the _how_. Since the examples focus on complex chords, you might ask right away: how do you build these chords?\n",
      "\n",
      "Chords and transient scales in JI can be built using variants of James Tenney's harmonic crystal growth technique[3](https://www.osar.fr/notes/justintonation#fn:3). It sounds fancy, but it's a natural process: you take a small number of \"seed\" tones (in the examples above and below, 2 seed tones were used for each chord), and you \"grow\" the set by finding tones that are most consonant with all the starting points. In this context, Tenney gives a precise definition of consonance in the form of [harmonic distance](https://en.xen.wiki/w/Tenney_height), but different definitions can and should be experimented with. Tenney also describes this process as iterative, where each addition uses the entire previous set as reference, but I prefer to measure distance from nothing but the seeds.\n",
      "\n",
      "This method can produce a huge variety of chords, especially when slightly modified or constrained in different ways. The relationship between the seeds is perhaps the main determinant for the \"feel\" of the final chord.\n",
      "\n",
      "The example above is representative of crystal growth, and shows another interesting aspect of this technique: it gives you a direct measure of which tones are central to the chord, and which ones are more peripheral. This can be exploited in composition: do you start with misleading tones, only to reveal the true nature of the chord later on? Or do need to make an impact and state the chord's root immediately? The \"order of growth\" makes it easy to find this balance, something which is used in this example.\n",
      "\n",
      "If you think this approach seems impractical to do \"by hand\", you're completely right. The broader question of which tools suit freestyle JI is a tough one. In my case, I somewhat sidestepped this issue by writing everything in code, for which there's currently no better documentation than [the source code itself](https://github.com/pac-dev/AmbientGardenAlbum). That said, nothing in this post is specific to algorithmic music, and I believe that programming should not be necessary to compose in freestyle JI.\n",
      "\n",
      "The topic of tools, and of deeper practical music theory for composing in this system, is something I would love to dive into, but that's firmly for another day (or lifetime, but we'll get there!) What's really missing here is the counterbalance: I've praised the light side of JI, so all that's left now is to **complain about the dark side**.\n",
      "\n",
      "Dissonance in JI: The Devil's Bargain\n",
      "-------------------------------------\n",
      "\n",
      "Rollercoaster Warning\n",
      "\n",
      "I'm now going to argue the complete opposite of everything written above.\n",
      "\n",
      "It's sometimes said that 12TET, or even temperaments in general, are mostly \"vestigial\" when composing digitally. After all, computers shouldn't be limited to a fixed number of keys, holes or frets like physical instruments. So in theory, one can imagine that JI offers strictly more freedom and consonance than 12TET. This is, of course, quite wrong, and in a way that has nothing to do with ease-of-use or tools.\n",
      "\n",
      "This point is best illustrated using an odd but very useful representation: harmonic lattices.\n",
      "\n",
      "A harmonic lattice in just intonation.  \n",
      "Values inside the cells are relative to some base frequency, in this case, 440Hz.\n",
      "\n",
      "To be precise, this is a piece of the infinite _5-limit pitch class lattice_ (a variant of the [tonnetz](https://en.wikipedia.org/wiki/Tonnetz)). Our reference pitch is in the middle. Moving right is equivalent to multiplying the current frequency by 3. Similarly, moving up is a multiplication by 5. All of the results are automatically transposed into a single octave. While more dimensions could be added, this choice of factors is good at representing \"mainstream\" harmony, and at placing more consonant tones closer together.\n",
      "\n",
      "Let's zoom out a bit to reveal the most important pattern on the lattice:\n",
      "\n",
      "Similar tones in just intonation.\n",
      "\n",
      "Playing on the lattice reveals an inevitable fact: some tones are very similar to each other. Clicking on a tone above highlights the most similar tones. These small intervals are _commas_, and they make composition in JI difficult, because we don't hear the harmonic relationship that defines them, instead, they appear to us as out-of tune versions of the same tone.\n",
      "\n",
      "Things get interesting when we represent tempered systems on the lattice. I mentioned that 12TET gets its harmonic significance from the way it approximates JI intervals. This can be heard and visualized by comparing the JI lattice against its corresponding 12TET approximation:\n",
      "\n",
      "Exactly equal tones in 12TET.\n",
      "\n",
      "In the 12TET lattice, those similar tones are now exactly equal, in other words, the commas are tempered. In fact, the 12 tones of 12TET are now repeated infinitely across the grid. **This is a tiling**, which opens up different possibilities for composition: you can harmoniously move in any direction, and magically end up back at your starting point. Let's look at this exact technique being used in the _Aníron_ chord progression from Howard Shore's soundtrack to The Lord of the Rings. Here, the chords are projected onto the 12TET lattice:\n",
      "\n",
      "The \"Aníron\" chord progression, from Howard Shore's soundtrack to The Lord of the Rings\n",
      "\n",
      "Above, you can see how harmonic motion in one direction \"magically\" returns to its starting point, thanks to 12TET's tiling. If you try to replicate this in JI, you're now fighting with commas:\n",
      "\n",
      "Solution 1 (drift):\n",
      "\n",
      "Solution 2 (jump):\n",
      "\n",
      "Attempting and failing to play the Aníron chord progression in JI.\n",
      "\n",
      "There are two ways of adapting this chord progression to JI:\n",
      "\n",
      "*   Keep moving in one direction, and become out of tune with your starting point. The result is somewhere between _unsatisfying_ and _jarring_ (\"Solution 1\" above).\n",
      "    \n",
      "*   Jump back to your starting point when you get near a tone that's roughly similar. This introduces an out-of-tune transition and defeats the consonance of JI (\"Solution 2\" above).\n",
      "    \n",
      "\n",
      "12TET naturally merges these small out-of-tune intervals, while distributing any required dissonance evenly over multiple tones. This property exists in other temperaments as well, but 12TET happens to be very good at it. This harmonic technique of \"traveling through\" 12TET's tiling is neither rare not advanced, it's something that musicians and composers do constantly without even thinking about it. Not only that, but it's common to use single chords that connect tiles together, which would also be impossible in JI. That's why a good part of the music that we know and love does not have any equivalent in JI.\n",
      "\n",
      "The more you compose in JI, the more you realize the extent of this problem. Even in some very simple chord progressions, our perception of harmony relies on multiple ambiguous connections between tones in different harmonic directions. JI creates perfect intervals for a single interpretation of how tones are connected, but we still hear the other possible interpretations, and they're now more dissonant than with 12TET!\n",
      "\n",
      "As a consequence, composing in JI is a bit like navigating a minefield. Hidden dissonant intervals are everywhere, and if you don't deliberately avoid them, you're almost guaranteed to simply make things worse than with 12TET. Once you've got a solid intuition for this, then yes, composing in JI can become harmonically rich and varied while remaining consonant.\n",
      "\n",
      "Thinking in Different Tunings\n",
      "-----------------------------\n",
      "\n",
      "This article started off with \"JI is better than 12TET\", then completely reversed positions. I'll cap off this shameless \"thesis/antithesis/synthesis\" structure by saying that tuning systems are like artistic construction tools: they all do different things, and while 12TET is great, it's a bit like a hammer that's currently being used for _everything_.\n",
      "\n",
      "There's also a finer point that underlies this entire article: just intonation is often used as an alternative tuning, but it's more interesting to see it as an entirely different way of composing. Most pieces written in 12TET do not convert well to JI. Similarly, you can do things in JI that don't map well to 12TET. This article gives examples of both.\n",
      "\n",
      "Music software often offers a \"tuning\" setting, and then lets you compose using the same old interface. This is interesting to play with, but it's not a good representation of the possibilities of JI, which really call for different ways of representing and reasoning about harmony.\n",
      "\n",
      "Further reading: It's a tone-eat-tone world out there\n",
      "-----------------------------------------------------\n",
      "\n",
      "*   [The Xenharmonic Wiki](https://en.xen.wiki/w/Main_Page) is a community of mad scientists focusing on alternative tunings. If you think this entire article is a false dichotomy between two arbitrarily chosen ways of tuning, you can go there to get your fill of additional tunings.\n",
      "*   [Joe Monzo's \"bingo-card lattice\" page](http://tonalsoft.com/enc/b/bingo.aspx) is the closest thing I've found to this article. His other articles and software are closely related to this topic, and are worth looking into.\n",
      "*   [William A. Sethares' Adaptive Tuning](https://sethares.engr.wisc.edu/papers/adaptun.html) should be mentioned as a technique that can offer consonance and variety similar to freestyle JI, even while composing in a simpler system.\n",
      "\n",
      "  \n",
      "\n",
      "* * *\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "url_data = parser.parse(url)\n",
    "print(url_data.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDF 解析器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用marker引擎会从huggingface hub下载模型，并使用模型进行解析\n",
    "# 如果遇到网络问题，尝试运行以下代码\n",
    "\n",
    "import os\n",
    "os.environ['CURL_CA_BUNDLE'] = ''\n",
    "os.environ['HF_ENDPOINT']= 'https://hf-mirror.com'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded detection model vikp/surya_det3 on device mps with dtype torch.float16\n",
      "Loaded detection model vikp/surya_layout3 on device mps with dtype torch.float16\n",
      "Loaded reading order model vikp/surya_order on device mps with dtype torch.float16\n",
      "Loaded recognition model vikp/surya_rec on device mps with dtype torch.float16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zephyr/anaconda3/envs/e2m/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded texify model to mps with torch.float16 dtype\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zephyr/anaconda3/envs/e2m/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:513: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from wisup_e2m import PdfParser\n",
    "\n",
    "pdf_path = \"./test.pdf\"\n",
    "parser = PdfParser(engine=\"marker\") # pdf engines: marker, unstructured, surya_layout "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Detecting bboxes: 100%|██████████| 3/3 [00:07<00:00,  2.39s/it]\n",
      "Detecting bboxes: 100%|██████████| 2/2 [00:04<00:00,  2.17s/it]\n",
      "Finding reading order: 100%|██████████| 2/2 [00:24<00:00, 12.37s/it]\n"
     ]
    }
   ],
   "source": [
    "pdf_data = parser.parse(pdf_path) # 默认会在 ./figure 文件夹下生成图片，可通过参数修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Attention Is All You Need\n",
      "\n",
      "| Ashish Vaswani∗ Google Brain   |                                                 |\n",
      "|--------------------------------|-------------------------------------------------|\n",
      "| avaswani@google.com            | Noam Shazeer∗ Google Brain                      |\n",
      "| noam@google.com                | Niki Parmar∗                                    |\n",
      "| Google Research                |                                                 |\n",
      "| nikip@google.com               | Jakob Uszkoreit∗ Google Research usz@google.com |\n",
      "\n",
      "| Llion Jones∗            |                                         |\n",
      "|-------------------------|-----------------------------------------|\n",
      "| Google Research         |                                         |\n",
      "| llion@google.com        | Aidan N. Gomez∗ † University of Toronto |\n",
      "| aidan@cs.toronto.edu    | Łukasz Kaiser∗ Google Brain             |\n",
      "| lukaszkaiser@google.com |                                         |\n",
      "\n",
      "Illia Polosukhin∗ ‡\n",
      "illia.polosukhin@gmail.com\n",
      "\n",
      "## Abstract\n",
      "\n",
      "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.\n",
      "\n",
      "## 1 Introduction\n",
      "\n",
      "Recurrent neural networks, long short-term memory [12] and gated recurrent [7] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [29, 2, 5]. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [31, 21, 13].\n",
      "\n",
      "∗Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.\n",
      "\n",
      "†Work performed while at Google Brain.\n",
      "\n",
      "‡Work performed while at Google Research.\n",
      "\n",
      "31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.\n",
      "\n",
      "Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states ht, as a function of the previous hidden state ht−1 and the input for position t. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Recent work has achieved significant improvements in computational efficiency through factorization tricks [18] and conditional computation [26], while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.\n",
      "\n",
      "Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 16]. In all but a few cases [22], however, such attention mechanisms are used in conjunction with a recurrent network.\n",
      "\n",
      "In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output.\n",
      "\n",
      "The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\n",
      "\n",
      "## 2 Background\n",
      "\n",
      "The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU\n",
      "[20], ByteNet [15] and ConvS2S [8], all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions [11]. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section 3.2.\n",
      "\n",
      "Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations [4, 22, 23, 19].\n",
      "\n",
      "End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [28].\n",
      "\n",
      "To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution. In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [14, 15] and [8].\n",
      "\n",
      "## 3 Model Architecture\n",
      "\n",
      "Most competitive neural sequence transduction models have an encoder-decoder structure [5, 2, 29].\n",
      "\n",
      "Here, the encoder maps an input sequence of symbol representations (x1*, ..., x*n) to a sequence of continuous representations z = (z1*, ..., z*n). Given z, the decoder then generates an output sequence (y1*, ..., y*m) of symbols one element at a time. At each step the model is auto-regressive\n",
      "[9], consuming the previously generated symbols as additional input when generating the next.\n",
      "\n",
      "The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.\n",
      "\n",
      "## 3.1 Encoder And Decoder Stacks\n",
      "\n",
      "Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-\n",
      "\n",
      "![figures/2_image_0.png](figures/2_image_0.png)\n",
      "\n",
      "wise fully connected feed-forward network. We employ a residual connection [10] around each of the two sub-layers, followed by layer normalization [1]. That is, the output of each sub-layer is LayerNorm(x + Sublayer(x)), where Sublayer(x) is the function implemented by the sub-layer itself. To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension dmodel = 512.\n",
      "\n",
      "Decoder: The decoder is also composed of a stack of N = 6 identical layers. In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization. We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position i can depend only on the known outputs at positions less than i.\n",
      "\n",
      "## 3.2 Attention\n",
      "\n",
      "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.\n",
      "\n",
      "## 3.2.1 Scaled Dot-Product Attention\n",
      "\n",
      "We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2). The input consists of queries and keys of dimension dk, and values of dimension dv. We compute the dot products of the\n",
      "\n",
      "![figures/3_image_0.png](figures/3_image_0.png)\n",
      "\n",
      "![figures/3_image_1.png](figures/3_image_1.png)\n",
      "\n",
      "query with all keys, divide each by √dk, and apply a softmax function to obtain the weights on the values.\n",
      "\n",
      "In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q. The keys and values are also packed together into matrices K and V . We compute the matrix of outputs as:\n",
      "\n",
      "$$\\mathrm{Attention}(Q,K,V)=\\mathrm{softmax}(\\frac{Q K^{T}}{\\sqrt{d_{k}}})V$$\n",
      "$$(1)$$\n",
      "\n",
      "The two most commonly used attention functions are additive attention [2], and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of √\n",
      "1 dk\n",
      ". Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.\n",
      "\n",
      "While for small values of dk the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of dk [3]. We suspect that for large values of dk, the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients 4. To counteract this effect, we scale the dot products by √\n",
      "1 dk\n",
      ".\n",
      "\n",
      "## 3.2.2 Multi-Head Attention\n",
      "\n",
      "Instead of performing a single attention function with dmodel-dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to dk, dk and dv dimensions, respectively. On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding dv-dimensional output values. These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2. Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.\n",
      "\n",
      "# Multihead(Q, K, V ) = Concat(Head1, ..., Headh)Wo Where Headi = Attention(Qwq I , Kw K I , V Wv I )\n",
      "\n",
      "Where the projections are parameter matrices W\n",
      "Q\n",
      "i ∈ R\n",
      "dmodel×dk , W K\n",
      "i ∈ R\n",
      "dmodel×dk , WV\n",
      "i ∈ R\n",
      "dmodel×dv and WO ∈ R\n",
      "hdv×dmodel.\n",
      "\n",
      "In this work we employ h = 8 parallel attention layers, or heads. For each of these we use dk = dv = dmodel/h = 64. Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.\n",
      "\n",
      "## 3.2.3 Applications Of Attention In Our Model\n",
      "\n",
      "The Transformer uses multi-head attention in three different ways:\n",
      "- In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as\n",
      "[31, 2, 8].\n",
      "\n",
      "- The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.\n",
      "\n",
      "- Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to −∞) all values in the input of the softmax which correspond to illegal connections. See Figure 2.\n",
      "\n",
      "## 3.3 Position-Wise Feed-Forward Networks\n",
      "\n",
      "In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.\n",
      "\n",
      "$$\\mathrm{FFN}(x)=\\operatorname*{max}(0,x W_{1}+b_{1})W_{2}+b_{2}$$\n",
      "FFN(x) = max(0*, xW*1 + b1)W2 + b2 (2)\n",
      "While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1.\n",
      "\n",
      "The dimensionality of input and output is dmodel = 512, and the inner-layer has dimensionality df f = 2048.\n",
      "\n",
      "## 3.4 Embeddings And Softmax\n",
      "\n",
      "Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension dmodel. We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [24]. In the embedding layers, we multiply those weights by √dmodel.\n",
      "\n",
      "## 3.5 Positional Encoding\n",
      "\n",
      "Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add \"positional encodings\" to the input embeddings at the\n",
      "\n",
      "| Layer Type                  | Complexity per Layer   | Sequential   | Maximum Path Length   |\n",
      "|-----------------------------|------------------------|--------------|-----------------------|\n",
      "| Operations                  |                        |              |                       |\n",
      "| Self-Attention              | O(n 2 · d)             | O(1)         | O(1)                  |\n",
      "| Recurrent                   | O(n · d 2 )            | O(n)         | O(n)                  |\n",
      "| Convolutional               | O(k · n · d 2 )        | O(1)         | O(logk(n))            |\n",
      "| Self-Attention (restricted) | O(r · n · d)           | O(1)         | O(n/r)                |\n",
      "\n",
      "bottoms of the encoder and decoder stacks. The positional encodings have the same dimension dmodel as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed [8].\n",
      "\n",
      "In this work, we use sine and cosine functions of different frequencies:\n",
      "\n",
      "$$PE_{(pos,2i)}=sin(pos/10000^{2i/d_{\\text{model}}})$$ $$PE_{(pos,2i+1)}=cos(pos/10000^{2i/d_{\\text{model}}})$$\n",
      "\n",
      "where pos is the position and i is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2π to 10000 · 2π. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, P Epos+k can be represented as a linear function of P Epos.\n",
      "\n",
      "We also experimented with using learned positional embeddings [8] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)). We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.\n",
      "\n",
      "## 4 Why Self-Attention\n",
      "\n",
      "In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations\n",
      "(x1*, ..., x*n) to another sequence of equal length (z1*, ..., z*n), with xi, zi ∈ R\n",
      "d, such as a hidden layer in a typical sequence transduction encoder or decoder. Motivating our use of self-attention we consider three desiderata.\n",
      "\n",
      "One is the total computational complexity per layer. Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.\n",
      "\n",
      "The third is the path length between long-range dependencies in the network. Learning long-range dependencies is a key challenge in many sequence transduction tasks. One key factor affecting the ability to learn such dependencies is the length of the paths forward and backward signals have to traverse in the network. The shorter these paths between any combination of positions in the input and output sequences, the easier it is to learn long-range dependencies [11]. Hence we also compare the maximum path length between any two input and output positions in networks composed of the different layer types.\n",
      "\n",
      "As noted in Table 1, a self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations. In terms of computational complexity, self-attention layers are faster than recurrent layers when the sequence length n is smaller than the representation dimensionality d, which is most often the case with sentence representations used by state-of-the-art models in machine translations, such as word-piece [31] and byte-pair [25] representations. To improve computational performance for tasks involving very long sequences, self-attention could be restricted to considering only a neighborhood of size r in the input sequence centered around the respective output position. This would increase the maximum path length to O(n/r). We plan to investigate this approach further in future work.\n",
      "\n",
      "A single convolutional layer with kernel width *k < n* does not connect all pairs of input and output positions. Doing so requires a stack of O(n/k) convolutional layers in the case of contiguous kernels, or O(logk(n)) in the case of dilated convolutions [15], increasing the length of the longest paths between any two positions in the network. Convolutional layers are generally more expensive than recurrent layers, by a factor of k. Separable convolutions [6], however, decrease the complexity considerably, to O(k · n · d + n · d 2). Even with k = n, however, the complexity of a separable convolution is equal to the combination of a self-attention layer and a point-wise feed-forward layer, the approach we take in our model.\n",
      "\n",
      "As side benefit, self-attention could yield more interpretable models. We inspect attention distributions from our models and present and discuss examples in the appendix. Not only do individual attention heads clearly learn to perform different tasks, many appear to exhibit behavior related to the syntactic and semantic structure of the sentences.\n",
      "\n",
      "## 5 Training\n",
      "\n",
      "This section describes the training regime for our models.\n",
      "\n",
      "## 5.1 Training Data And Batching\n",
      "\n",
      "We trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding [3], which has a shared sourcetarget vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT\n",
      "2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary [31]. Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.\n",
      "\n",
      "## 5.2 Hardware And Schedule\n",
      "\n",
      "We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models,(described on the bottom line of table 3), step time was 1.0 seconds. The big models were trained for 300,000 steps\n",
      "(3.5 days).\n",
      "\n",
      "## 5.3 Optimizer\n",
      "\n",
      "We used the Adam optimizer [17] with β1 = 0.9, β2 = 0.98 and  = 10−9. We varied the learning rate over the course of training, according to the formula:\n",
      "lrate = d\n",
      "−0.5 model · min(step_num−0.5, step_num · warmup_*steps*−1.5) (3)\n",
      "This corresponds to increasing the learning rate linearly for the first warmup_*steps* training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used warmup_*steps* = 4000.\n",
      "\n",
      "## 5.4 Regularization\n",
      "\n",
      "We employ three types of regularization during training:\n",
      "Residual Dropout We apply dropout [27] to the output of each sub-layer, before it is added to the sub-layer input and normalized. In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of P*drop* = 0.1.\n",
      "\n",
      "$$({\\mathfrak{I}})$$\n",
      "\n",
      "| English-to-German and English-to-French newstest2014 tests at a fraction of the training cost. BLEU Training Cost (FLOPs) Model EN-DE EN-FR EN-DE EN-FR ByteNet [15] 23.75 Deep-Att + PosUnk [32] 39.2 1.0 · 1020 GNMT + RL [31] 24.6 39.92 2.3 · 1019 1.4 · 1020 ConvS2S [8] 25.16 40.46 9.6 · 1018 1.5 · 1020 MoE [26] 26.03 40.56 2.0 · 1019 1.2 · 1020 Deep-Att + PosUnk Ensemble [32] 40.4 8.0 · 1020 GNMT + RL Ensemble [31] 26.30 41.16 1.8 · 1020 1.1 · 1021 ConvS2S Ensemble [8] 26.36 41.29 7.7 · 1019 1.2 · 1021 Transformer (base model) 27.3 38.1 3.3 · 1018 Transformer (big) 28.4 41.0 2.3 · 1019   |\n",
      "|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
      "\n",
      "Label Smoothing During training, we employed label smoothing of value ls = 0.1 [30]. This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.\n",
      "\n",
      "## 6 Results 6.1 Machine Translation\n",
      "\n",
      "On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big)\n",
      "in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3. Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.\n",
      "\n",
      "On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate P*drop* = 0.1, instead of 0.3.\n",
      "\n",
      "For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals. For the big models, we averaged the last 20 checkpoints. We used beam search with a beam size of 4 and length penalty α = 0.6 [31]. These hyperparameters were chosen after experimentation on the development set. We set the maximum output length during inference to input length + 50, but terminate early when possible [31].\n",
      "\n",
      "Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature. We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU 5.\n",
      "\n",
      "## 6.2 Model Variations\n",
      "\n",
      "To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the development set, newstest2013. We used beam search as described in the previous section, but no checkpoint averaging. We present these results in Table 3. In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2. While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.\n",
      "\n",
      "| N     | dmodel                                    | dff   | h    | dk   | dv   | Pdrop   | ls      | train   | PPL   | BLEU   | params   |    |\n",
      "|-------|-------------------------------------------|-------|------|------|------|---------|------|---------|-------|--------|----------|----|\n",
      "| steps | (dev)                                     | (dev) | ×106 |      |      |         |      |         |       |        |          |    |\n",
      "| base  | 6                                         | 512   | 2048 | 8    | 64   | 64      | 0.1  | 0.1     | 100K  | 4.92   | 25.8     | 65 |\n",
      "| 1     | 512                                       | 512   | 5.29 | 24.9 |      |         |      |         |       |        |          |    |\n",
      "| 4     | 128                                       | 128   | 5.00 | 25.5 |      |         |      |         |       |        |          |    |\n",
      "| (A)   | 16                                        | 32    | 32   | 4.91 | 25.8 |         |      |         |       |        |          |    |\n",
      "| 32    | 16                                        | 16    | 5.01 | 25.4 |      |         |      |         |       |        |          |    |\n",
      "| (B)   | 16                                        | 5.16  | 25.1 | 58   |      |         |      |         |       |        |          |    |\n",
      "| 32    | 5.01                                      | 25.4  | 60   |      |      |         |      |         |       |        |          |    |\n",
      "| 2     | 6.11                                      | 23.7  | 36   |      |      |         |      |         |       |        |          |    |\n",
      "| 4     | 5.19                                      | 25.3  | 50   |      |      |         |      |         |       |        |          |    |\n",
      "| 8     | 4.88                                      | 25.5  | 80   |      |      |         |      |         |       |        |          |    |\n",
      "| 256   | 32                                        | 32    | 5.75 | 24.5 | 28   |         |      |         |       |        |          |    |\n",
      "| 1024  | 128                                       | 128   | 4.66 | 26.0 | 168  |         |      |         |       |        |          |    |\n",
      "| 1024  | 5.12                                      | 25.4  | 53   |      |      |         |      |         |       |        |          |    |\n",
      "| 4096  | 4.75                                      | 26.2  | 90   |      |      |         |      |         |       |        |          |    |\n",
      "| (C)   | 0.0                                       | 5.77  | 24.6 |      |      |         |      |         |       |        |          |    |\n",
      "| 0.2   | 4.95                                      | 25.5  |      |      |      |         |      |         |       |        |          |    |\n",
      "| (D)   | 0.0                                       | 4.67  | 25.3 |      |      |         |      |         |       |        |          |    |\n",
      "| 0.2   | 5.47                                      | 25.7  |      |      |      |         |      |         |       |        |          |    |\n",
      "| (E)   | positional embedding instead of sinusoids | 4.92  | 25.7 |      |      |         |      |         |       |        |          |    |\n",
      "| big   | 6                                         | 1024  | 4096 | 16   | 0.3  | 300K    | 4.33 | 26.4    | 213   |        |          |    |\n",
      "\n",
      "In Table 3 rows (B), we observe that reducing the attention key size dk hurts model quality. This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial. We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting. In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [8], and observe nearly identical results to the base model.\n",
      "\n",
      "## 7 Conclusion\n",
      "\n",
      "In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\n",
      "\n",
      "For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.\n",
      "\n",
      "We are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video. Making generation less sequential is another research goals of ours.\n",
      "\n",
      "The code we used to train and evaluate our models is available at https://github.com/\n",
      "tensorflow/tensor2tensor.\n",
      "\n",
      "Acknowledgements We are grateful to Nal Kalchbrenner and Stephan Gouws for their fruitful comments, corrections and inspiration.\n",
      "\n",
      "## References\n",
      "\n",
      "[1] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.\n",
      "\n",
      "[2] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. *CoRR*, abs/1409.0473, 2014.\n",
      "\n",
      "[3] Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc V. Le. Massive exploration of neural machine translation architectures. *CoRR*, abs/1703.03906, 2017.\n",
      "\n",
      "[4] Jianpeng Cheng, Li Dong, and Mirella Lapata. Long short-term memory-networks for machine reading. *arXiv preprint arXiv:1601.06733*, 2016.\n",
      "\n",
      "[5] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. *CoRR*, abs/1406.1078, 2014.\n",
      "\n",
      "[6] Francois Chollet. Xception: Deep learning with depthwise separable convolutions. *arXiv* preprint arXiv:1610.02357, 2016.\n",
      "\n",
      "[7] Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. *CoRR*, abs/1412.3555, 2014.\n",
      "\n",
      "[8] Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolutional sequence to sequence learning. *arXiv preprint arXiv:1705.03122v2*, 2017.\n",
      "\n",
      "[9] Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.\n",
      "\n",
      "[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016.\n",
      "\n",
      "[11] Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and Jürgen Schmidhuber. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001.\n",
      "\n",
      "[12] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. *Neural computation*,\n",
      "9(8):1735–1780, 1997.\n",
      "\n",
      "[13] Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring the limits of language modeling. *arXiv preprint arXiv:1602.02410*, 2016.\n",
      "\n",
      "[14] Łukasz Kaiser and Ilya Sutskever. Neural GPUs learn algorithms. In *International Conference* on Learning Representations (ICLR), 2016.\n",
      "\n",
      "[15] Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu. Neural machine translation in linear time. *arXiv preprint arXiv:1610.10099v2*,\n",
      "2017.\n",
      "\n",
      "[16] Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. Structured attention networks.\n",
      "\n",
      "In *International Conference on Learning Representations*, 2017.\n",
      "\n",
      "[17] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In *ICLR*, 2015. [18] Oleksii Kuchaiev and Boris Ginsburg. Factorization tricks for LSTM networks. arXiv preprint arXiv:1703.10722, 2017.\n",
      "\n",
      "[19] Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130, 2017.\n",
      "\n",
      "[20] Samy Bengio Łukasz Kaiser. Can active memory replace attention? In Advances in Neural Information Processing Systems, (NIPS), 2016.\n",
      "\n",
      "[21] Minh-Thang Luong, Hieu Pham, and Christopher D Manning. Effective approaches to attentionbased neural machine translation. *arXiv preprint arXiv:1508.04025*, 2015.\n",
      "\n",
      "[22] Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. A decomposable attention model. In *Empirical Methods in Natural Language Processing*, 2016.\n",
      "\n",
      "[23] Romain Paulus, Caiming Xiong, and Richard Socher. A deep reinforced model for abstractive summarization. *arXiv preprint arXiv:1705.04304*, 2017.\n",
      "\n",
      "[24] Ofir Press and Lior Wolf. Using the output embedding to improve language models. *arXiv* preprint arXiv:1608.05859, 2016.\n",
      "\n",
      "[25] Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. *arXiv preprint arXiv:1508.07909*, 2015.\n",
      "\n",
      "[26] Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. *arXiv preprint arXiv:1701.06538*, 2017.\n",
      "\n",
      "[27] Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958, 2014.\n",
      "\n",
      "[28] Sainbayar Sukhbaatar, arthur szlam, Jason Weston, and Rob Fergus. End-to-end memory networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2440–2448. Curran Associates, Inc., 2015.\n",
      "\n",
      "[29] Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. Sequence to sequence learning with neural networks. In *Advances in Neural Information Processing Systems*, pages 3104–3112, 2014.\n",
      "\n",
      "[30] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna.\n",
      "\n",
      "Rethinking the inception architecture for computer vision. *CoRR*, abs/1512.00567, 2015.\n",
      "\n",
      "[31] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016.\n",
      "\n",
      "[32] Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with fast-forward connections for neural machine translation. *CoRR*, abs/1606.04199, 2016.\n"
     ]
    }
   ],
   "source": [
    "print(pdf_data.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PPT 解析器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果需要解析PPT、DOC文件，则需要下载`libreoffice`依赖，具体安装方法如下：\n",
    "\n",
    "#### Mac\n",
    "    \n",
    "```bash\n",
    "brew install libreoffice\n",
    "```\n",
    "\n",
    "#### Linux\n",
    "\n",
    "```bash\n",
    "sudo apt-get install libreoffice\n",
    "```\n",
    "\n",
    "#### Windows\n",
    "\n",
    "进入官网 https://www.libreoffice.org/ 下载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wisup_e2m import PptParser\n",
    "\n",
    "ppt_path = \"./test.ppt\"\n",
    "parser = PptParser(engine=\"unstructured\") # pdf engines: unstructured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ppt_data = parser.parse(ppt_path) # 默认会在 ./figure 文件夹下生成图片，可通过参数修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Application example: \n",
      "![](figures/17_5.png)\n",
      "![](figures/17_4.png)\n",
      "![](figures/17_3.png)\n",
      "![](figures/17_2.png)\n",
      "![](figures/17_1.png)\n",
      "![](figures/17_0.png)\n",
      "![](figures/5_1.jpg)\n",
      "![](figures/5_0.jpg)\n",
      "![](figures/1_0.jpg)\n",
      "# Photo OCR\n",
      "# Problem description and pipeline\n",
      "# Machine Learning\n",
      "# The Photo OCR problem\n",
      "![](figures/2_4.jpg)\n",
      "![](figures/2_3.png)\n",
      "![](figures/2_2.png)\n",
      "![](figures/2_1.png)\n",
      "![](figures/2_0.png)\n",
      "# LULA B’s ANTIQUE MALL\n",
      "# LULA B’s\n",
      "# OPEN\n",
      "# LULA B’s\n",
      "# Photo OCR pipeline\n",
      "![](figures/16_0.jpg)\n",
      "![](figures/4_0.jpg)\n",
      "# 1. Text detection\n",
      "# 2. Character segmentation\n",
      "# 3. Character classification\n",
      "# A\n",
      "# T\n",
      "# N\n",
      "# Photo OCR pipeline\n",
      "# Character segmentation\n",
      "# Character recognition\n",
      "![](figures/18_0.png)\n",
      "# Image\n",
      "# Text detection\n",
      "![](figures/14_12.png)\n",
      "![](figures/14_11.png)\n",
      "![](figures/14_10.png)\n",
      "![](figures/14_9.png)\n",
      "![](figures/14_8.png)\n",
      "![](figures/14_7.png)\n",
      "![](figures/14_6.png)\n",
      "![](figures/14_5.png)\n",
      "![](figures/14_4.png)\n",
      "![](figures/14_3.png)\n",
      "![](figures/14_2.png)\n",
      "![](figures/14_1.png)\n",
      "![](figures/14_0.png)\n",
      "![](figures/13_2.png)\n",
      "![](figures/13_1.png)\n",
      "![](figures/13_0.png)\n",
      "![](figures/12_3.png)\n",
      "![](figures/12_2.png)\n",
      "![](figures/12_1.png)\n",
      "![](figures/12_0.png)\n",
      "# Application example: \n",
      "# Photo OCR\n",
      "# Sliding windows\n",
      "# Machine Learning\n",
      "# Pedestrian detection\n",
      "![](figures/6_12.png)\n",
      "![](figures/6_11.png)\n",
      "![](figures/6_10.png)\n",
      "![](figures/6_9.png)\n",
      "![](figures/6_8.png)\n",
      "![](figures/6_7.png)\n",
      "![](figures/6_6.png)\n",
      "![](figures/6_5.png)\n",
      "![](figures/6_4.png)\n",
      "![](figures/6_3.png)\n",
      "![](figures/6_2.png)\n",
      "![](figures/6_1.jpg)\n",
      "![](figures/6_0.png)\n",
      "# Text detection\n",
      "Supervised learning for pedestrian detection\n",
      "![](figures/7_0.jpg)\n",
      "# pixels in 82x36 image patches\n",
      "# Negative examples\n",
      "# Positive examples\n",
      "Sliding window detection\n",
      "![](figures/11_0.jpg)\n",
      "![](figures/10_0.jpg)\n",
      "![](figures/9_0.jpg)\n",
      "![](figures/8_0.jpg)\n",
      "Sliding window detection\n",
      "Sliding window detection\n",
      "Sliding window detection\n",
      "# Text detection\n",
      "# Text detection\n",
      "# Negative examples\n",
      "# Positive examples\n",
      "# Text detection\n",
      "1D Sliding window for character segmentation\n",
      "![](figures/15_4.jpg)\n",
      "![](figures/15_3.png)\n",
      "![](figures/15_2.png)\n",
      "![](figures/15_1.png)\n",
      "![](figures/15_0.png)\n",
      "# Negative examples\n",
      "# Positive examples\n",
      "# Photo OCR pipeline\n",
      "# 1. Text detection\n",
      "# 2. Character segmentation\n",
      "# 3. Character classification\n",
      "# A\n",
      "# T\n",
      "# N\n",
      "# Application example: \n",
      "# Photo OCR\n",
      "# Getting lots of data: Artificial data synthesis\n",
      "# Machine Learning\n",
      "# Character recognition\n",
      "# T\n",
      "# A\n",
      "# N\n",
      "# I\n",
      "# Q\n",
      "# A\n",
      "# Artificial data synthesis for photo OCR\n",
      "![](figures/20_1.png)\n",
      "![](figures/20_0.png)\n",
      "![](figures/19_1.png)\n",
      "![](figures/19_0.png)\n",
      "# Abcdefg\n",
      "# Abcdefg\n",
      "# Abcdefg\n",
      "# Abcdefg\n",
      "# Abcdefg\n",
      "# Real data\n",
      "# [Adam Coates and Tao Wang]\n",
      "# Artificial data synthesis for photo OCR\n",
      "# Synthetic data\n",
      "# Real data\n",
      "# [Adam Coates and Tao Wang]\n",
      "Synthesizing data by introducing distortions\n",
      "# [Adam Coates and Tao Wang]\n",
      "Synthesizing data by introducing distortions: Speech recognition\n",
      "![](figures/22_3.jpg)\n",
      "![](figures/22_2.png)\n",
      "![](figures/22_1.png)\n",
      "![](figures/22_0.png)\n",
      "# Original audio:\n",
      "# Audio on bad cellphone connection\n",
      "# Noisy background: Crowd\n",
      "# Noisy background: Machinery\n",
      "# [www.pdsounds.org]\n",
      "Synthesizing data by introducing distortions    \n",
      "Distortion introduced should be representation of the type of noise/distortions in the test set.\n",
      "# Audio:\n",
      "# Background noise, \n",
      "# bad cellphone connection\n",
      "Usually does not help to add purely random/meaningless noise to your data.\n",
      "# intensity (brightness) of pixel\n",
      "#         random noise\n",
      "# [Adam Coates and Tao Wang]\n",
      "Discussion on getting more data\n",
      "![](figures/25_0.jpg)\n",
      "Make sure you have a low bias classifier before expending the effort. (Plot learning curves). E.g. keep increasing the number of features/number of hidden units in neural network until you have a low bias classifier.\n",
      "“How much work would it be to get 10x as much data as we currently have?”\n",
      "Artificial data synthesis\n",
      "Collect/label it yourself\n",
      "“Crowd source” (E.g. Amazon Mechanical Turk)\n",
      "Discussion on getting more data\n",
      "Make sure you have a low bias classifier before expending the effort. (Plot learning curves). E.g. keep increasing the number of features/number of hidden units in neural network until you have a low bias classifier.\n",
      "“How much work would it be to get 10x as much data as we currently have?”\n",
      "Artificial data synthesis\n",
      "Collect/label it yourself\n",
      "“Crowd source” (E.g. Amazon Mechanical Turk)\n",
      "# Application example: \n",
      "# Photo OCR\n",
      "# Ceiling analysis: What part of the pipeline to work on next\n",
      "# Machine Learning\n",
      "Estimating the errors due to each component (ceiling analysis)\n",
      "![](figures/27_1.jpg)\n",
      "![](figures/27_0.jpg)\n",
      "# Character segmentation\n",
      "# Character recognition\n",
      "# Image\n",
      "# Text detection\n",
      "What part of the pipeline should you spend the most time trying to improve?\n",
      "Component               Accuracy\n",
      "Overall system          72%\n",
      "Text detection          89%\n",
      "Character segmentation  90%\n",
      "Character recognition   100%\n",
      "# Another ceiling analysis example\n",
      "# Face recognition from images \n",
      "# (Artificial example)\n",
      "# Camera\n",
      "# image\n",
      "# Preprocess\n",
      "(remove background)\n",
      "# Eyes segmentation\n",
      "# Logistic regression\n",
      "# Nose segmentation\n",
      "# Label\n",
      "# Face detection\n",
      "# Mouth segmentation\n",
      "# Another ceiling analysis example\n",
      "# Camera\n",
      "# image\n",
      "# Preprocess\n",
      "(remove background)\n",
      "# Eyes segmentation\n",
      "# Logistic regression\n",
      "# Label\n",
      "# Nose segmentation\n",
      "# Face detection\n",
      "Component                       Accuracy\n",
      "Overall system                  85%\n",
      "Preprocess (remove background)  85.1%\n",
      "Face detection                  91%\n",
      "Eyes segmentation               95%\n",
      "Nose segmentation               96%\n",
      "Mouth segmentation              97%\n",
      "Logistic regression             100%\n",
      "# Mouth segmentation\n"
     ]
    }
   ],
   "source": [
    "print(ppt_data.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DOCX 解析器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wisup_e2m import DocxParser\n",
    "\n",
    "docx_path = \"./test.docx\"\n",
    "parser = DocxParser(engine=\"pandoc\") # pdf engines: pandoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "docx_data = parser.parse(docx_path) # 默认会在 ./figure 文件夹下生成图片，可通过参数修改"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "McKinsey 7S OER\n",
      "The McKinsey 7S framework is a valuable tool used by organizations to ensure their company is operating optimally under a variety of circumstances. Developed by the McKinsey consulting company in the 1980s, the tool sought to promote an alignment of seven key parameters as a means for maximizing the likelihood of company success. These parameters, dubbed the “7 S’s”, are all interconnected within the framework, which each “S” being as important as the next. This interconnectedness also outlines a key component of the framework – a change in one parameter necessitates a change in all of the others in order to maintain alignment and ensure company success. The model is particularly useful when re-evaluating a company’s organizational structure and strategy after a large change (ex: a merger, change in management, rapid expansion etc.). Such changes will likely cause a large shift in at least one of the 7 “S” components. It is therefore useful to use the McKinsey 7S model to re-evaluate whether all 7 S components remain aligned, and make the necessary changes to maintain this synergy if misalignment has occurred. \n",
      "The 7 S elements are outlined in the matrix below:\n",
      "                                \n",
      "![image1.jpg](figures/image1.jpg)\n",
      "These elements can be further subdivided into the “Hard S’s” and “Soft S’s”. \n",
      "Hard S’s\n",
      "Strategy\n",
      "A company’s “strategy” is the plan it has developed in order to achieve a sustained competitive advantage within their market space. This strategy should be specific and well articulated. When building a company strategy, it is important to consider the following questions:\n",
      "What are the company objectives?\n",
      "How will the objectives be achieved?\n",
      "What makes us competitive?\n",
      "How will the company mitigate risks and potentially damaging external factors?\n",
      "Structure\n",
      "The structure of a company is its organizational layout. This includes the way different business subdivisions and units are organized, as well as the hierarchy of who is accountable to whom and for what. This is an important factor in determining the decision making process and lines of communication within a company.\n",
      "Systems\n",
      "Systems are procedures and processes that “run” an organization. These are the areas of the company that determine how business is done. Examples of systems include:\n",
      "Financial Systems\n",
      "HR Systems\n",
      "Document & Information Storage\n",
      "Communication Systems \n",
      "Systems also include the controls and feedback mechanisms used by an organization to ensure they are “on the right track”. \n",
      "\t\n",
      "Soft S’s\n",
      "\t\n",
      "Style\n",
      "\tStyle is a representation of the management of a company and how they expect their employees to represent themselves. These mannerisms reflect how the company wants to be portrayed to the public and is generally a direct reflection of the character of the company’s leaders. Some examples include:\n",
      "How people within the company interact with one another and their customers\n",
      "Any symbolic value attached to actions and decisions\n",
      "How leaders and managers approach decision making (i.e. democratically)\n",
      "Staff\n",
      "\tStaff is everything to do with company personnel, from the top to the bottom of an organization. One major theme when considering the staff of a company is the motivation, and generally companies tend to build their staff based on how people get motivated. Some examples of staffing to consider are:\n",
      "What type of employees the company has/wants\n",
      "How many employees are needed for the organization to succeed\n",
      "How the employees are trained \n",
      "How will the company reward or incentivize employees\n",
      "Skills\n",
      "\tSkills are the abilities in which the employees perform the best, and the overall repertoire of talent that the company has. These skills can include:\n",
      "Hard skills (i.e. software developers, skilled laborers, etc.)\n",
      "Communication within the company and to their customers\n",
      "Wealth of knowledge (i.e. PhDs, experienced workers, etc.)\n",
      "Major changes within the organization often leads to a reassessment of what skills are present and what skills are needed moving forward. Skill development is often equally as important as hiring people who are extremely skilled.\n",
      "Shared Values\n",
      "Shared values are at the core of the McKinsey 7s Model, with each of the other six S’s having a direct connection with it. Shared values are the overall standards to which management and employees hold themselves to, and these must be clear and evident to the entire organization. Quite often an organization will use a mission statement to try to reflect these values to the public. Essentially these are the ‘guiding principles’ of a company and relate to everything that happens anywhere within the organization.\n",
      "Applying Mckinsey 7s to Coca Cola\n",
      "\tIn order to visualize how the Mckinsey 7s model would work on a real company, we have chosen to apply the principles to the beverage company Coca Cola.\n",
      "Strategy\n",
      "\t\n",
      "\tThe strategy at Coca Cola can be broken down into many different sub categories. The corporate strategy, business strategy, and operational strategy can all be considered key moving pieces in the success of Coca Cola’s business model. The corporate strategy looks at continuing to build Coke’s extensive beverage portfolio, asset expansion into asia, and developing new products that align with the current demand for more natural and healthy alternative beverages.\n",
      "Structure\n",
      "\tThe corporate structure of Coca Cola is heavily segmented to account for the many different divisions and regions this multi-billion-dollar companies operates in. Head office provides the main direction for the company, with key strategic decisions being made by the team of 12 ExCo’s with one head ExCo committee serving as the CEO. The ExCo’s either represent one of the regional branches of Coca Cola or serve a more specialized niche in the company (e.g. CFO). Latin America, Pacific, Eurasia & Africa, Russia, and North. America represent the five key regions that Coca Cola has designated specialized business units for. \n",
      "Systems\n",
      "\tThere are several main systems that are part of Coca-Cola business strategy. The have directional systems that flow from ExCo’s down to entry level employees where upper management ensures the shared values is adhered to when making any strategic decisions. Process systems are in place to divide up large tasks into bite sized chunks and to provide management with the necessary tools to adhere to the shared values. Additionally, there are day to day management systems in place to receive employee feedback and provide incentives and awards for adherence to the shared values.\n",
      "Style\n",
      "\tThe corporate culture of Coca Cola emphasizes a style that focuses on teamwork and togetherness. They continual look to implement the idea of One team, one company, One passion. Coca Cola strives to promotes sustainability and community development, as well as being environmentally friendly.\n",
      "Staff\n",
      "\tDue to Coca-Cola’s tremendous standing in the soft drink industry, they draw in some of the most talented people in the industry. Coca-Cola also provides career development opportunities, stock options, and performance rewards resulting in a high retention rate and low employee turnover.\n",
      "Skills\n",
      "\tAs previously mentioned, Coca-Cola has some of the most talented employees in the soft drink industry, thus the skills their workers possess are second to none. Coca-Cola is always coming up with innovative ways to improve their products and this reflects the great management, research and development, and systems skills that are prevalent through out the company. They also always look to allocate whatever technological and staffing resources are required to make their innovative ideas a reality. \n",
      "Shared Values\n",
      "\tAs previously mentioned, the shared values are at the core of McKinsey 7s Model. Coca-Cola has clearly recognized this and look to implement their own shared values in their systems, structure, strategy and style as a world leader in the soft drink industry. Before making any key business and management decision, Coca-Cola will ensure the decision at hand meets their 6p’s:\n",
      "People: Be a great place to work where people are inspired to be the best they can be.\n",
      "Portfolio: Bring to the world a portfolio of quality beverage brands that anticipate and satisfy people's desires and needs.\n",
      "Partners: Nurture a winning network of customers and suppliers, together we create mutual, enduring value.\n",
      "Planet: Be a responsible citizen that makes a difference by helping build and support sustainable communities.\n",
      "Profit: Maximize long-term return to shareowners while being mindful of our overall responsibilities.\n",
      "Productivity: Be a highly effective, lean and fast-moving organization.\n",
      "Evidently, Coca-Cola employs a business model that has considered the many key factors presented in the Mckinsey 7s model. The alignment of Coca-Cola to these principals has likely driven the success of this company making them the world leader in the soft-drink industry they are today. \n",
      "The First Order: A Case Study\n",
      "Introduction\n",
      "\tA Long time ago in a galaxy far far away a series of strategic management missteps led to the downfall of the largest provider of jobs the galaxy had ever seen. Following the death of both Emperor Palpatine and Darth Vader, the loss of strategic planets and the battle at Jakku the organization known as ‘The Empire’ collapsed. Remnants of the organization fled to the outskirts of the galaxy. Under new management the organization rebranded itself the ‘First Order’ and began to amass new resources including raw materials, and employees. The First Order was founded with the stated goal of bringing order to the galaxy much like ‘The Empire’ before it. According to their internal documents and media the First Order was formed as an organization to combat the ineffectual and corrupt ways of the New Republic. In the years following the First Order has achieved many of its primary goals. It has wrested control of many star systems from the New Republic. Following years of technological development the First Order used its R&D project ‘Starkiller base’ to destroy the planets which were the seats of power in the New Republic. However this aggressive expansion strategy led to the formation of the Resistance. The Resistance was formed by General Leia Organa as a direct competitor to the First Order. It used its small size and band of plucky dissidents to destroy many of the First Order’s technologically superior weapons including Starkiller base and the mobile headquarters of the First Order, The Supremacy. The supreme leader of the First Order has now been replaced by his immediate subordinate Kylo Ren via violent means. Given the massive organizational change including new leadership, a massive loss of resources and a rising competitor the First Order is in need of a paradigm shift if it is to remain a viable organization.\n",
      "Using McKinsey 7s to Evaluate the First Order \n",
      "Following his promotion to supreme leader, Kylo Ren realized he must change the structure of The First Order if he was to truly bring peace to the galaxy. He observed that the Resistance was a small nimble organization which had been able to out-maneuver his own organization at almost every turn. Kylo is a smart manager however, and he realized that no two organizations are alike, he can not simply copy the strategies of the Resistance in order to out compete them. He had find a way to evaluate the First Order on its own terms. General Hux, an ambitious young executive suggested they use McKinsey 7s as a tool to evaluate the alignment of their organizational design. What follows is a description of the organization and its abilities that Kylo Ren used in order to determine the alignment of McKinsey 7s. \n",
      "First Order Overview\n",
      "The First Order is a military junta, its leaders are all military and there are no civilians who participate in the power structure of the organization. There is a strict hierarchy in the organization much like any military organization. There is a rank system in the First Order ranging from general down to sergeant, if a higher ranking officer gives an order it is to be followed without question. The rank and file employees of the First Order bear the title ‘stormtrooper’. Stormtroopers are recruited at birth, and live their entire lives within the organization. Each stormtrooper is given a numerical designation and they are not referred to by any other name. A former employee of the First Order, General Brendol Hux, had a vision of the perfect soldier and his vision became the training program for the First Order Stormtroopers. They are provided with First Order approved education in history, first aid, combat training, and political science. \n",
      "Technology and Strategy\n",
      "While the First Order is descended from the Galactic Empire it does not possess the same vast resources of the latter. In order to remain competitive the First Order has had a strategy of quality over quantity. It has focused its resources on the development of new weapons technologies and the development of an elite training program for its employees. Rather than swarming its enemies the First Order makes calculated strikes. In the past the First Order has relied on the corruption of the New Republic and their general trusting nature to operate under the radar. However, following recent events the First Order is completely in the spotlight and the stated competitor of the Resistance. \n",
      "Management \n",
      "Former supreme leader Snoke created a culture of fear of ,and extreme loyalty to the First Order. On one hand he vilified anything outside of the First Order and also punished any dissent from within with extreme prejudice. This top down pressure was exerted through three main members of upper management, Kylo Ren, General Armitage Hux and Captain Phasma. All members of this triumvirate were quick to punish with threat of or actual physical violence including execution. At present only Kylo Ren and General Hux are part of the organization following the deaths of both Snoke and Phasma. They are both left with an organization in disarray and waning morale.  \n",
      "Rank and File Employees \n",
      "Given the hierarchical structure of the organization Kylo Ren has not been concerned with the general well being of employees and this shows. While dissent is quashed with impunity there is one famous case that serves as an example for what can go wrong with the First Orders training system. Stormtrooper FN-2187 (AKA Finn) was unable to conform to the brutal nature of stormtrooper program and left the organization to join their direct competitor, the Resistance. FN-2187 cited the cruelty of upper management and the fact that he had now choice in actually joining the First Order as he was recruited at birth. While employed with the Resistance FN-2187 was allowed more freedom and rose in the ranks very quickly to become a key member of the organization. His actions directly resulted in major losses for the First Order. The former employee FN-2187 is described as a traitor by most current employees of the First Order. He has taken the training and knowledge they provided and turned it against them. Knowledge such as the location of critical systems on their starships, and secret facilities across the galaxy. It is not within the training or ability of the First Order employees to understand or forgive FN-2187. \n",
      "Activity:\n",
      "It is your job to help Kylo Ren make sense of the alignment of his organization and find ways to change it in order to align all of the McKinsey 7s so the First Order may return order to the galaxy and crush the resistance. Follow the steps below to finish this task.\n",
      "Step 1. Identify misaligned areas\n",
      "First off fill out this table to determine the alignment of the McKinsey 7s. One cell has been filled out for you. \n",
      "Step 2. Determine the optimal design \n",
      "Following the identification of misaligned areas, determine which segments need to be changed in the organizational design to achieve alignment. It is important to note that all the areas of organization are interconnected and should be given equal importance. It is useful to start with shared values and strategy as these areas provide the foundation for the other areas. On top of shared values and strategy the structure and systems generally come next followed by skills, staff and style. \n",
      "Step 3. Decide where and what changes should be made\n",
      "Give specific examples of how changes could be made. \n"
     ]
    }
   ],
   "source": [
    "print(docx_data.text) # 能发现图片以 ![]() 格式被插入了text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 核心功能2: 转换器\n",
    "\n",
    "转换器（Converter）的目的是在解析成功后。将文本或图片进行转换，目前支持通过各类模型`engine`引擎转换成Markdown。\n",
    "\n",
    "目前支持`litellm`和`zhipuai`，后续会支持更多引擎\n",
    "\n",
    "- Litellm官网查看支持的模型:\n",
    "  - https://docs.litellm.ai/docs/providers/\n",
    "- Zhipuai官网查看支持的模型:\n",
    "  - https://open.bigmodel.cn/dev/howuse/model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 文本转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wisup_e2m import TextConverter\n",
    "\n",
    "text_converter = TextConverter(\n",
    "    engine=\"litellm\",\n",
    "    api_key=\"<your api key>\",\n",
    "    model=\"deepseek/deepseek-chat\",\n",
    "    caching=True,\n",
    "    cache_type=\"disk-cache\",\n",
    ")\n",
    "\n",
    "raw_text = docx_data.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "✨ 目前转换器仅支持默认策略\n",
    "在 `default` 默认策略下，大模型会先判断**文本类型**和**文本格式**，在进行接龙式的转换，以保证Markdown文本生成的连续性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "推断结果：\n",
      "\n",
      "文本类型：商业分析报告/案例研究\n",
      "\n",
      "文本格式：\n",
      "- 一级标题: **McKinsey 7S OER**\n",
      "- 二级标题: **Applying Mckinsey 7s to Coca Cola**\n",
      "- 三级标题: **The First Order: A Case Study**\n",
      "- 四级标题: **Using McKinsey 7s to Evaluate the First Order**\n",
      "- 小标题（不符合结构）: **Hard S’s**, **Soft S’s**, **Management**, **Rank and File Employees**, **Activity:**\n",
      "\n",
      "解释：\n",
      "- 文本主要讨论了McKinsey 7S框架在不同组织中的应用，包括Coca Cola和The First Order的案例研究，因此推断为商业分析报告或案例研究。\n",
      "- 文本中的标题结构遵循了一级到四级的层次结构，但部分小标题如“Hard S’s”和“Soft S’s”等不符合标准结构，因此采用加粗方式标记。None**McKinsey 7S OER**\n",
      "\n",
      "The McKinsey 7S framework is a valuable tool used by organizations to ensure their company is operating optimally under a variety of circumstances. Developed by the McKinsey consulting company in the 1980s, the tool sought to promote an alignment of seven key parameters as a means for maximizing the likelihood of company success. These parameters, dubbed the “7 S’s”, are all interconnected within the framework, with each “S” being as important as the next. This interconnectedness also outlines a key component of the framework – a change in one parameter necessitates a change in all of the others in order to maintain alignment and ensure company success. The model is particularly useful when re-evaluating a company’s organizational structure and strategy after a large change (ex: a merger, change in management, rapid expansion etc.). Such changes will likely cause a large shift in at least one of the 7 “S” components. It is therefore useful to use the McKinsey 7S model to re-evaluate whether all 7 S components remain aligned, and make the necessary changes to maintain this synergy if misalignment has occurred.\n",
      "\n",
      "The 7 S elements are outlined in the matrix below:\n",
      "\n",
      "These elements can be further subdivided into the “Hard S’s” and “Soft S’s”.\n",
      "\n",
      "**Hard S’s**\n",
      "\n",
      "**Strategy**\n",
      "\n",
      "A company’s “strategy” is the plan it has developed in order to achieve a sustained competitive advantage within their market space. This strategy should be specific and well articulated. When building a company strategy, it is important to consider the following questions:\n",
      "- What are the company objectives?\n",
      "- How will the objectives be achieved?\n",
      "- What makes us competitive?\n",
      "- How will the company mitigate risks and potentially damaging external factors?\n",
      "\n",
      "**Structure**\n",
      "\n",
      "The structure of a company is its organizational layout. This includes the way different business subdivisions and units are organized, as well as the hierarchy of who is accountable to whom and for what. This is an important factor in determining the decision making process and lines of communication within a company.\n",
      "\n",
      "**Systems**\n",
      "\n",
      "Systems are procedures and processes that “run” an organization. These are the areas of the company that determine how business is done. Examples of systems include:\n",
      "- Financial Systems\n",
      "- HR Systems\n",
      "- Document & Information Storage\n",
      "- Communication Systems\n",
      "\n",
      "Systems also include the controls and feedback mechanisms used by an organization to ensure they are “on the right track”.\n",
      "\n",
      "**Soft S’s**\n",
      "\n",
      "**Style**\n",
      "\n",
      "Style is a representation of the management of a company and how they expect their employees to represent themselves. These mannerisms reflect how the company wants to be portrayed to the public and is generally a direct reflection of the character of the company’s leaders. Some examples include:\n",
      "- How people within the company interact with one another and their customers\n",
      "- Any symbolic value attached to actions and decisions\n",
      "- How leaders and managers approach decision making (i.e. democratically)\n",
      "\n",
      "**Staff**\n",
      "\n",
      "Staff is everything to do with company personnel, from the top to the bottom of an organization. One major theme when considering the staff of a company is the motivation, and generally companies tend to build their staff based on how people get motivated. Some examples of staffing to consider are:\n",
      "- What type of employees the company has/wants\n",
      "- How many employees are needed for the organization to succeed\n",
      "- How the employees are trained\n",
      "- How will the company reward or incentivize employees\n",
      "\n",
      "**Skills**\n",
      "\n",
      "Skills are the abilities in which the employees perform the best, and the overall repertoire of talent that the company has. These skills can include:\n",
      "- Hard skills (i.e. software developers, skilled laborers, etc.)\n",
      "- Communication within the company and to their customers\n",
      "- Wealth of knowledge (i.e. PhDs, experienced workers, etc.)\n",
      "\n",
      "Major changes within the organization often leads to a reassessment of what skills are present and what skills are needed moving forward. Skill development is often equally as important as hiring people who are extremely skilled.\n",
      "\n",
      "**Shared Values**\n",
      "\n",
      "Shared values are at the core of the McKinsey 7s Model, with each of the other six S’s having a direct connection with it. Shared values are the overall standards to which management and employees hold themselves to, and these must be clear and evident to the entire organization. Quite often an organization will use a mission statement to try to reflect these values to the public. Essentially these are the ‘guiding principles’ of a company and relate to everything that happens anywhere within the organization.\n",
      "\n",
      "**Applying Mckinsey 7s to Coca Cola**\n",
      "\n",
      "In order to visualize how the Mckinsey 7s model would work on a real company, we have chosen to apply the principles to the beverage company Coca Cola.\n",
      "\n",
      "**Strategy**\n",
      "\n",
      "The strategy at Coca Cola can be broken down into many different sub categories. The corporate strategy, business strategy, and operational strategy can all be considered key moving pieces in the success of Coca Cola’s business model. The corporate strategy looks at continuing to build Coke’s extensive beverage portfolio, asset expansion into asia, and developing new products that align with the current demand for more natural and healthy alternative beverages.\n",
      "\n",
      "**Structure**\n",
      "\n",
      "The corporate structure of Coca Cola is heavily segmented to account for the many different divisions and regions this multi-billion-dollar companies operates in. Head office provides the main direction for the company, with key strategic decisions being made by the team of 12 ExCo’s with one head ExCo committee serving as the CEO. The ExCo’s either represent one of the regional branches of Coca Cola or serve a more specialized niche in the company (e.g. CFO). Latin America, Pacific, Eurasia & Africa, Russia, and North. America represent the five key regions that Coca Cola has designated specialized business units for.\n",
      "\n",
      "**Systems**\n",
      "\n",
      "There are several main systems that are part of Coca-Cola business strategy. The have directional systems that flow from ExCo’s down to entry level employees where upper management ensures the shared values is adhered to when making any strategic decisions. Process systems are in place to divide up large tasks into bite sized chunks and to provide management with the necessary tools to adhere to the shared values. Additionally, there are day to day management systems in place to receive employee feedback and provide incentives and awards for adherence to the shared values.\n",
      "\n",
      "**Style**\n",
      "\n",
      "The corporate culture of Coca Cola emphasizes a style that focuses on teamwork and togetherness. They continual look to implement the idea of One team, one company, One passion. Coca Cola strives to promotes sustainability and community development, as well as being environmentally friendly.\n",
      "\n",
      "**Staff**\n",
      "\n",
      "Due to Coca-Cola’s tremendous standing in the soft drink industry, they draw in some of the most talented people in the industry. Coca-Cola also provides career development opportunities, stock options, and performance rewards resulting in a high retention rate and low employee turnover.\n",
      "\n",
      "**Skills**\n",
      "\n",
      "As previously mentioned, Coca-Cola has some of the most talented employees in the soft drink industry, thus the skills their workers possess are second to none. Coca-Cola is always coming up with innovative ways to improve their products and this reflects the great management, research and development, and systems skills that are prevalent through out the company. They also always look to allocate whatever technological and staffing resources are required to make their innovative ideas a reality.\n",
      "\n",
      "**Shared Values**\n",
      "\n",
      "As previously mentioned, the shared values are at the core of McKinsey 7s Model. Coca-Cola has clearly recognized this and look to implement their own shared values in their systems, structure, strategy and style as a world leader in the soft drink industry. Before making any key business and management decision, Coca-Cola will ensure the decision at hand meets their 6p’s:\n",
      "- People: Be a great place to work where people are inspired to be the best they can be.\n",
      "- Portfolio: Bring to the world a portfolio of quality beverage brands that anticipate and satisfy people's desires and needs.\n",
      "- Partners: Nurture a winning network of customers and suppliers, together we create mutual, enduring value.\n",
      "- Planet: Be a responsible citizen that makes a difference by helping build and support sustainable communities.\n",
      "- Profit: Maximize long-term return to shareowners while being mindful of our overall responsibilities.\n",
      "- Productivity: Be a highly effective, lean and fast-moving organization.\n",
      "\n",
      "Evidently, Coca-Cola employs a business model that has considered the many key factors presented in the Mckinsey 7s model. The alignment of Coca-Cola to these principals has likely driven the success of this company making them the world leader in the soft-drink industry they are today.\n",
      "\n",
      "**The First Order: A Case Study**\n",
      "\n",
      "**Introduction**\n",
      "\n",
      "A Long time ago in a galaxy far far away a series of strategic management missteps led to the downfall of the largest provider of jobs the galaxy had ever seen. Following the death of both Emperor Palpatine and Darth Vader, the loss of strategic planets and the battle at Jakku the organization known as ‘The Empire’ collapsed. Remnants of the organization fled to the outskirts of the galaxy. Under new management the organization rebranded itself the ‘First Order’ and began to amass new resources including raw materials, and employees. The First Order was founded with the stated goal of bringing order to the galaxy much like ‘The Empire’ before it. According to their internal documents and media the First Order was formed as an organization to combat the ineffectual and corrupt ways of the New Republic. In the years following the First Order has achieved many of its primary goals. It has wrested control of many star systems from the New Republic. Following years of technological development the First Order used its R&D project ‘Starkiller base’ to destroy the planets which were the seats of power in the New Republic. However this aggressive expansion strategy led to the formation of the Resistance. The Resistance was formed by General Leia Organa as a direct competitor to the First Order. It used its small size and band of plucky dissidents to destroy many of the First Order’s technologically superior weapons including Starkiller base and the mobile headquarters of the First Order, The Supremacy. The supreme leader of the First Order has now been replaced by his immediate subordinate Kylo Ren via violent means. Given the massive organizational change including new leadership, a massive loss of resources and a rising competitor the First Order is in need of a paradigm shift if it is to remain a viable organization.\n",
      "\n",
      "**Using McKinsey 7s to Evaluate the First Order**\n",
      "\n",
      "Following his promotion to supreme leader, Kylo Ren realized he must change the structure of The First Order if he was to truly bring peace to the galaxy. He observed that the Resistance was a small nimble organization which had been able to out-maneuver his own organization at almost every turn. Kylo is a smart manager however, and he realized that no two organizations are alike, he can not simply copy the strategies of the Resistance in order to out compete them. He had find a way to evaluate the First Order on its own terms. General Hux, an ambitious young executive suggested they use McKinsey 7s as a tool to evaluate the alignment of their organizational design. What follows is a description of the organization and its abilities that Kylo Ren used in order to determine the alignment of McKinsey 7s.\n",
      "\n",
      "**First Order Overview**\n",
      "\n",
      "The First Order is a military junta, its leaders are all military and there are no civilians who participate in the power structure of the organization. There is a strict hierarchy in the organization much like any military organization. There is a rank system in the First Order ranging from general down to sergeant, if a higher ranking officer gives an order it is to be followed without question. The rank and file employees of the First Order bear the title ‘stormtrooper’. Stormtroopers are recruited at birth, and live their entire lives within the organization. Each stormtrooper is given a numerical designation and they are not referred to by any other name. A former employee of the First Order, General Brendol Hux, had a vision of the perfect soldier and his vision became the training program for the First Order Stormtroopers. They are provided with First Order approved education in history, first aid, combat training, and political science.\n",
      "\n",
      "**Technology and Strategy**\n",
      "\n",
      "While the First Order is descended from the Galactic Empire it does not possess the same vast resources of the latter. In order to remain competitive the First Order has had a strategy of quality over quantity. It has focused its resources on the development of new weapons technologies and the development of an elite training program for its employees. Rather than swarming its enemies the First Order makes calculated strikes. In the past the First Order has relied on the corruption of the New Republic and their general trusting nature to operate under the radar. However, following recent events the First Order is completely in the spotlight and the stated competitor of the Resistance.\n",
      "\n",
      "**Management**\n",
      "\n",
      "Former supreme leader Snoke created a culture of fear of ,and extreme loyalty to the First Order. On one hand he vilified anything outside of the First Order and also punished any dissent from within with extreme prejudice. This top down pressure was exerted through three main members of upper management, Kylo Ren, General Armitage Hux and Captain Phasma. All members of this triumvirate were quick to punish with threat of or actual physical violence including execution. At present only Kylo Ren and General Hux are part of the organization following the deaths of both Snoke and Phasma. They are both left with an organization in disarray and waning morale.\n",
      "\n",
      "**Rank and File Employees**\n",
      "\n",
      "Given the hierarchical structure of the organization Kylo Ren has not been concerned with the general well being of employees and this shows. While dissent is quashed with impunity there is one famous case that serves as an example for what can go wrong with the First Orders training system. Stormtrooper FN-2187 (AKA Finn) was unable to conform to the brutal nature of stormtrooper program and left the organization to join their direct competitor, the Resistance. FN-2187 cited the cruelty of upper management and the fact that he had now choice in actually joining the First Order as he was recruited at birth. While employed with the Resistance FN-2187 was allowed more freedom and rose in the ranks very quickly to become a key member of the organization. His actions directly resulted in major losses for the First Order. The former employee FN-2187 is described as a traitor by most current employees of the First Order. He has taken the training and knowledge they provided and turned it against them. Knowledge such as the location of critical systems on their starships, and secret facilities across the galaxy. It is not within the training or ability of the First Order employees to understand or forgive FN-2187.\n",
      "\n",
      "**Activity:**\n",
      "\n",
      "It is your job to help Kylo Ren make sense of the alignment of his organization and find ways to change it in order to align all of the McKinsey 7s so the First Order may return order to the galaxy and crush the resistance. Follow the steps below to finish this task.\n",
      "\n",
      "**Step 1. Identify misaligned areas**\n",
      "\n",
      "First off fill out this table to determine the alignment of the McKinsey 7s. One cell has been filled out for you.\n",
      "\n",
      "| Hard or Soft S | Current State | Aligned ? |\n",
      "|----------------|---------------|-----------|\n",
      "| Strategy       | Order, Might, Loyalty, Power | - |\n",
      "| Structure      | - | - |\n",
      "| Style          | - | - |\n",
      "| Staff          | - | - |\n",
      "| Skills         | - | - |\n",
      "| Systems        | - | - |\n",
      "| Shared Values  | - | - |\n",
      "\n",
      "**Step 2. Determine the optimal design**\n",
      "\n",
      "Following the identification of misaligned areas, determine which segments need to be changed in the organizational design to achieve alignment. It is important to note that all the areas of organization are interconnected and should be given equal importance. It is useful to start with shared values and strategy as these areas provide the foundation for the other areas. On top of shared values and strategy the structure and systems generally come next followed by skills, staff and style.\n",
      "\n",
      "**Step 3. Decide where and what changes should be made**\n",
      "\n",
      "Give specific examples of how changes could be made.\n",
      "\n",
      "<NEWLINE>None"
     ]
    }
   ],
   "source": [
    "markdown_text = text_converter.convert(raw_text) # 默认  strategy = \"default\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**McKinsey 7S OER**\n",
      "\n",
      "The McKinsey 7S framework is a valuable tool used by organizations to ensure their company is operating optimally under a variety of circumstances. Developed by the McKinsey consulting company in the 1980s, the tool sought to promote an alignment of seven key parameters as a means for maximizing the likelihood of company success. These parameters, dubbed the “7 S’s”, are all interconnected within the framework, with each “S” being as important as the next. This interconnectedness also outlines a key component of the framework – a change in one parameter necessitates a change in all of the others in order to maintain alignment and ensure company success. The model is particularly useful when re-evaluating a company’s organizational structure and strategy after a large change (ex: a merger, change in management, rapid expansion etc.). Such changes will likely cause a large shift in at least one of the 7 “S” components. It is therefore useful to use the McKinsey 7S model to re-evaluate whether all 7 S components remain aligned, and make the necessary changes to maintain this synergy if misalignment has occurred.\n",
      "\n",
      "The 7 S elements are outlined in the matrix below:\n",
      "\n",
      "These elements can be further subdivided into the “Hard S’s” and “Soft S’s”.\n",
      "\n",
      "**Hard S’s**\n",
      "\n",
      "**Strategy**\n",
      "\n",
      "A company’s “strategy” is the plan it has developed in order to achieve a sustained competitive advantage within their market space. This strategy should be specific and well articulated. When building a company strategy, it is important to consider the following questions:\n",
      "- What are the company objectives?\n",
      "- How will the objectives be achieved?\n",
      "- What makes us competitive?\n",
      "- How will the company mitigate risks and potentially damaging external factors?\n",
      "\n",
      "**Structure**\n",
      "\n",
      "The structure of a company is its organizational layout. This includes the way different business subdivisions and units are organized, as well as the hierarchy of who is accountable to whom and for what. This is an important factor in determining the decision making process and lines of communication within a company.\n",
      "\n",
      "**Systems**\n",
      "\n",
      "Systems are procedures and processes that “run” an organization. These are the areas of the company that determine how business is done. Examples of systems include:\n",
      "- Financial Systems\n",
      "- HR Systems\n",
      "- Document & Information Storage\n",
      "- Communication Systems\n",
      "\n",
      "Systems also include the controls and feedback mechanisms used by an organization to ensure they are “on the right track”.\n",
      "\n",
      "**Soft S’s**\n",
      "\n",
      "**Style**\n",
      "\n",
      "Style is a representation of the management of a company and how they expect their employees to represent themselves. These mannerisms reflect how the company wants to be portrayed to the public and is generally a direct reflection of the character of the company’s leaders. Some examples include:\n",
      "- How people within the company interact with one another and their customers\n",
      "- Any symbolic value attached to actions and decisions\n",
      "- How leaders and managers approach decision making (i.e. democratically)\n",
      "\n",
      "**Staff**\n",
      "\n",
      "Staff is everything to do with company personnel, from the top to the bottom of an organization. One major theme when considering the staff of a company is the motivation, and generally companies tend to build their staff based on how people get motivated. Some examples of staffing to consider are:\n",
      "- What type of employees the company has/wants\n",
      "- How many employees are needed for the organization to succeed\n",
      "- How the employees are trained\n",
      "- How will the company reward or incentivize employees\n",
      "\n",
      "**Skills**\n",
      "\n",
      "Skills are the abilities in which the employees perform the best, and the overall repertoire of talent that the company has. These skills can include:\n",
      "- Hard skills (i.e. software developers, skilled laborers, etc.)\n",
      "- Communication within the company and to their customers\n",
      "- Wealth of knowledge (i.e. PhDs, experienced workers, etc.)\n",
      "\n",
      "Major changes within the organization often leads to a reassessment of what skills are present and what skills are needed moving forward. Skill development is often equally as important as hiring people who are extremely skilled.\n",
      "\n",
      "**Shared Values**\n",
      "\n",
      "Shared values are at the core of the McKinsey 7s Model, with each of the other six S’s having a direct connection with it. Shared values are the overall standards to which management and employees hold themselves to, and these must be clear and evident to the entire organization. Quite often an organization will use a mission statement to try to reflect these values to the public. Essentially these are the ‘guiding principles’ of a company and relate to everything that happens anywhere within the organization.\n",
      "\n",
      "**Applying Mckinsey 7s to Coca Cola**\n",
      "\n",
      "In order to visualize how the Mckinsey 7s model would work on a real company, we have chosen to apply the principles to the beverage company Coca Cola.\n",
      "\n",
      "**Strategy**\n",
      "\n",
      "The strategy at Coca Cola can be broken down into many different sub categories. The corporate strategy, business strategy, and operational strategy can all be considered key moving pieces in the success of Coca Cola’s business model. The corporate strategy looks at continuing to build Coke’s extensive beverage portfolio, asset expansion into asia, and developing new products that align with the current demand for more natural and healthy alternative beverages.\n",
      "\n",
      "**Structure**\n",
      "\n",
      "The corporate structure of Coca Cola is heavily segmented to account for the many different divisions and regions this multi-billion-dollar companies operates in. Head office provides the main direction for the company, with key strategic decisions being made by the team of 12 ExCo’s with one head ExCo committee serving as the CEO. The ExCo’s either represent one of the regional branches of Coca Cola or serve a more specialized niche in the company (e.g. CFO). Latin America, Pacific, Eurasia & Africa, Russia, and North. America represent the five key regions that Coca Cola has designated specialized business units for.\n",
      "\n",
      "**Systems**\n",
      "\n",
      "There are several main systems that are part of Coca-Cola business strategy. The have directional systems that flow from ExCo’s down to entry level employees where upper management ensures the shared values is adhered to when making any strategic decisions. Process systems are in place to divide up large tasks into bite sized chunks and to provide management with the necessary tools to adhere to the shared values. Additionally, there are day to day management systems in place to receive employee feedback and provide incentives and awards for adherence to the shared values.\n",
      "\n",
      "**Style**\n",
      "\n",
      "The corporate culture of Coca Cola emphasizes a style that focuses on teamwork and togetherness. They continual look to implement the idea of One team, one company, One passion. Coca Cola strives to promotes sustainability and community development, as well as being environmentally friendly.\n",
      "\n",
      "**Staff**\n",
      "\n",
      "Due to Coca-Cola’s tremendous standing in the soft drink industry, they draw in some of the most talented people in the industry. Coca-Cola also provides career development opportunities, stock options, and performance rewards resulting in a high retention rate and low employee turnover.\n",
      "\n",
      "**Skills**\n",
      "\n",
      "As previously mentioned, Coca-Cola has some of the most talented employees in the soft drink industry, thus the skills their workers possess are second to none. Coca-Cola is always coming up with innovative ways to improve their products and this reflects the great management, research and development, and systems skills that are prevalent through out the company. They also always look to allocate whatever technological and staffing resources are required to make their innovative ideas a reality.\n",
      "\n",
      "**Shared Values**\n",
      "\n",
      "As previously mentioned, the shared values are at the core of McKinsey 7s Model. Coca-Cola has clearly recognized this and look to implement their own shared values in their systems, structure, strategy and style as a world leader in the soft drink industry. Before making any key business and management decision, Coca-Cola will ensure the decision at hand meets their 6p’s:\n",
      "- People: Be a great place to work where people are inspired to be the best they can be.\n",
      "- Portfolio: Bring to the world a portfolio of quality beverage brands that anticipate and satisfy people's desires and needs.\n",
      "- Partners: Nurture a winning network of customers and suppliers, together we create mutual, enduring value.\n",
      "- Planet: Be a responsible citizen that makes a difference by helping build and support sustainable communities.\n",
      "- Profit: Maximize long-term return to shareowners while being mindful of our overall responsibilities.\n",
      "- Productivity: Be a highly effective, lean and fast-moving organization.\n",
      "\n",
      "Evidently, Coca-Cola employs a business model that has considered the many key factors presented in the Mckinsey 7s model. The alignment of Coca-Cola to these principals has likely driven the success of this company making them the world leader in the soft-drink industry they are today.\n",
      "\n",
      "**The First Order: A Case Study**\n",
      "\n",
      "**Introduction**\n",
      "\n",
      "A Long time ago in a galaxy far far away a series of strategic management missteps led to the downfall of the largest provider of jobs the galaxy had ever seen. Following the death of both Emperor Palpatine and Darth Vader, the loss of strategic planets and the battle at Jakku the organization known as ‘The Empire’ collapsed. Remnants of the organization fled to the outskirts of the galaxy. Under new management the organization rebranded itself the ‘First Order’ and began to amass new resources including raw materials, and employees. The First Order was founded with the stated goal of bringing order to the galaxy much like ‘The Empire’ before it. According to their internal documents and media the First Order was formed as an organization to combat the ineffectual and corrupt ways of the New Republic. In the years following the First Order has achieved many of its primary goals. It has wrested control of many star systems from the New Republic. Following years of technological development the First Order used its R&D project ‘Starkiller base’ to destroy the planets which were the seats of power in the New Republic. However this aggressive expansion strategy led to the formation of the Resistance. The Resistance was formed by General Leia Organa as a direct competitor to the First Order. It used its small size and band of plucky dissidents to destroy many of the First Order’s technologically superior weapons including Starkiller base and the mobile headquarters of the First Order, The Supremacy. The supreme leader of the First Order has now been replaced by his immediate subordinate Kylo Ren via violent means. Given the massive organizational change including new leadership, a massive loss of resources and a rising competitor the First Order is in need of a paradigm shift if it is to remain a viable organization.\n",
      "\n",
      "**Using McKinsey 7s to Evaluate the First Order**\n",
      "\n",
      "Following his promotion to supreme leader, Kylo Ren realized he must change the structure of The First Order if he was to truly bring peace to the galaxy. He observed that the Resistance was a small nimble organization which had been able to out-maneuver his own organization at almost every turn. Kylo is a smart manager however, and he realized that no two organizations are alike, he can not simply copy the strategies of the Resistance in order to out compete them. He had find a way to evaluate the First Order on its own terms. General Hux, an ambitious young executive suggested they use McKinsey 7s as a tool to evaluate the alignment of their organizational design. What follows is a description of the organization and its abilities that Kylo Ren used in order to determine the alignment of McKinsey 7s.\n",
      "\n",
      "**First Order Overview**\n",
      "\n",
      "The First Order is a military junta, its leaders are all military and there are no civilians who participate in the power structure of the organization. There is a strict hierarchy in the organization much like any military organization. There is a rank system in the First Order ranging from general down to sergeant, if a higher ranking officer gives an order it is to be followed without question. The rank and file employees of the First Order bear the title ‘stormtrooper’. Stormtroopers are recruited at birth, and live their entire lives within the organization. Each stormtrooper is given a numerical designation and they are not referred to by any other name. A former employee of the First Order, General Brendol Hux, had a vision of the perfect soldier and his vision became the training program for the First Order Stormtroopers. They are provided with First Order approved education in history, first aid, combat training, and political science.\n",
      "\n",
      "**Technology and Strategy**\n",
      "\n",
      "While the First Order is descended from the Galactic Empire it does not possess the same vast resources of the latter. In order to remain competitive the First Order has had a strategy of quality over quantity. It has focused its resources on the development of new weapons technologies and the development of an elite training program for its employees. Rather than swarming its enemies the First Order makes calculated strikes. In the past the First Order has relied on the corruption of the New Republic and their general trusting nature to operate under the radar. However, following recent events the First Order is completely in the spotlight and the stated competitor of the Resistance.\n",
      "\n",
      "**Management**\n",
      "\n",
      "Former supreme leader Snoke created a culture of fear of ,and extreme loyalty to the First Order. On one hand he vilified anything outside of the First Order and also punished any dissent from within with extreme prejudice. This top down pressure was exerted through three main members of upper management, Kylo Ren, General Armitage Hux and Captain Phasma. All members of this triumvirate were quick to punish with threat of or actual physical violence including execution. At present only Kylo Ren and General Hux are part of the organization following the deaths of both Snoke and Phasma. They are both left with an organization in disarray and waning morale.\n",
      "\n",
      "**Rank and File Employees**\n",
      "\n",
      "Given the hierarchical structure of the organization Kylo Ren has not been concerned with the general well being of employees and this shows. While dissent is quashed with impunity there is one famous case that serves as an example for what can go wrong with the First Orders training system. Stormtrooper FN-2187 (AKA Finn) was unable to conform to the brutal nature of stormtrooper program and left the organization to join their direct competitor, the Resistance. FN-2187 cited the cruelty of upper management and the fact that he had now choice in actually joining the First Order as he was recruited at birth. While employed with the Resistance FN-2187 was allowed more freedom and rose in the ranks very quickly to become a key member of the organization. His actions directly resulted in major losses for the First Order. The former employee FN-2187 is described as a traitor by most current employees of the First Order. He has taken the training and knowledge they provided and turned it against them. Knowledge such as the location of critical systems on their starships, and secret facilities across the galaxy. It is not within the training or ability of the First Order employees to understand or forgive FN-2187.\n",
      "\n",
      "**Activity:**\n",
      "\n",
      "It is your job to help Kylo Ren make sense of the alignment of his organization and find ways to change it in order to align all of the McKinsey 7s so the First Order may return order to the galaxy and crush the resistance. Follow the steps below to finish this task.\n",
      "\n",
      "**Step 1. Identify misaligned areas**\n",
      "\n",
      "First off fill out this table to determine the alignment of the McKinsey 7s. One cell has been filled out for you.\n",
      "\n",
      "| Hard or Soft S | Current State | Aligned ? |\n",
      "|----------------|---------------|-----------|\n",
      "| Strategy       | Order, Might, Loyalty, Power | - |\n",
      "| Structure      | - | - |\n",
      "| Style          | - | - |\n",
      "| Staff          | - | - |\n",
      "| Skills         | - | - |\n",
      "| Systems        | - | - |\n",
      "| Shared Values  | - | - |\n",
      "\n",
      "**Step 2. Determine the optimal design**\n",
      "\n",
      "Following the identification of misaligned areas, determine which segments need to be changed in the organizational design to achieve alignment. It is important to note that all the areas of organization are interconnected and should be given equal importance. It is useful to start with shared values and strategy as these areas provide the foundation for the other areas. On top of shared values and strategy the structure and systems generally come next followed by skills, staff and style.\n",
      "\n",
      "**Step 3. Decide where and what changes should be made**\n",
      "\n",
      "Give specific examples of how changes could be made.\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(markdown_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 图片转换器\n",
    "\n",
    "图片转换器(`ImageConverter`)通过多模态大模型进行识别，可配合`PdfParser`的`surya_layout`引擎进行更细致的图片识别。\n",
    "\n",
    "以layout为后缀的布局识别引擎不会产生text，其核心是识别图片中的布局，然后截出无法被转换为文本的图片，并标识在图片上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zephyr/anaconda3/envs/e2m/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from wisup_e2m import PdfParser, ImageConverter\n",
    "\n",
    "work_dir = os.getcwd()\n",
    "image_dir = os.path.join(work_dir, \"figure\")\n",
    "\n",
    "test_surya_layout_pdf = \"./test_surya_layout_pdf.pdf\"\n",
    "\n",
    "# 加载解析器\n",
    "pdf_parser = PdfParser(engine=\"surya_layout\")\n",
    "\n",
    "image_converter = ImageConverter(\n",
    "    engine=\"litellm\",\n",
    "    api_key=\"<you api key>\",\n",
    "    model=\"gpt-4o\",\n",
    "    base_url=\"<you base url>\",\n",
    "    caching=True,\n",
    "    cache_type=\"disk-cache\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 解析PDF为图片\n",
    "test_surya_layout_pdf_data = pdf_parser.parse(\n",
    "    test_surya_layout_pdf,\n",
    "    start_page=0,\n",
    "    end_page=20,\n",
    "    work_dir=work_dir,\n",
    "    image_dir=image_dir, # 提取的图片保存的地方\n",
    "    relative_path=True, # 图片路径是否为相对路径(相对于work_dir)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': '',\n",
       " 'images': ['/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/0.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/1.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/2.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/3.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/4.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/5.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/6.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/7.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/8.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/9.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/10.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/11.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/12.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/13.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/14.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/15.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/16.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/17.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/18.png',\n",
       "  '/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/19.png'],\n",
       " 'attached_images': ['figure/0_0.png',\n",
       "  'figure/14_0.png',\n",
       "  'figure/16_0.png',\n",
       "  'figure/17_0.png',\n",
       "  'figure/19_0.png'],\n",
       " 'attached_images_map': {'0.png': ['figure/0_0.png'],\n",
       "  '1.png': [],\n",
       "  '2.png': [],\n",
       "  '3.png': [],\n",
       "  '4.png': [],\n",
       "  '5.png': [],\n",
       "  '6.png': [],\n",
       "  '7.png': [],\n",
       "  '8.png': [],\n",
       "  '9.png': [],\n",
       "  '10.png': [],\n",
       "  '11.png': [],\n",
       "  '12.png': [],\n",
       "  '13.png': [],\n",
       "  '14.png': ['figure/14_0.png'],\n",
       "  '15.png': [],\n",
       "  '16.png': ['figure/16_0.png'],\n",
       "  '17.png': ['figure/17_0.png'],\n",
       "  '18.png': [],\n",
       "  '19.png': ['figure/19_0.png']},\n",
       " 'metadata': {'engine': 'surya_layout',\n",
       "  'surya_layout_metadata': [{'bboxes': [{'polygon': [[100, 883],\n",
       "       [530, 841],\n",
       "       [568, 1177],\n",
       "       [137, 1219]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [100, 883, 530, 1177]},\n",
       "     {'polygon': [[176, 1485], [478, 1485], [478, 1585], [176, 1585]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [176, 1485, 478, 1585]},\n",
       "     {'polygon': [[194, 70], [576, 70], [576, 264], [194, 264]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [194, 70, 576, 264]},\n",
       "     {'polygon': [[81, 489], [143, 489], [143, 636], [81, 636]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [81, 489, 143, 636]},\n",
       "     {'polygon': [[189, 507], [1010, 507], [1010, 660], [189, 660]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [189, 507, 1010, 660]},\n",
       "     {'polygon': [[193, 699], [813, 699], [813, 809], [193, 809]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [193, 699, 813, 809]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 15,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-001'},\n",
       "   {'bboxes': [{'polygon': [[905, 154], [1017, 154], [1017, 200], [905, 200]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [905, 154, 1017, 200]},\n",
       "     {'polygon': [[126, 247], [816, 247], [816, 287], [126, 287]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [126, 247, 816, 287]},\n",
       "     {'polygon': [[127, 366], [303, 366], [303, 400], [127, 400]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 366, 303, 400]},\n",
       "     {'polygon': [[127, 416], [585, 416], [585, 477], [127, 477]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 416, 585, 477]},\n",
       "     {'polygon': [[128, 486], [405, 486], [405, 547], [128, 547]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 486, 405, 547]},\n",
       "     {'polygon': [[127, 556], [522, 556], [522, 615], [127, 615]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 556, 522, 615]},\n",
       "     {'polygon': [[127, 652], [334, 652], [334, 686], [127, 686]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 652, 334, 686]},\n",
       "     {'polygon': [[127, 702], [721, 702], [721, 763], [127, 763]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 702, 721, 763]},\n",
       "     {'polygon': [[125, 154], [790, 154], [790, 199], [125, 199]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Title',\n",
       "      'bbox': [125, 154, 790, 199]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 17,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-002'},\n",
       "   {'bboxes': [{'polygon': [[90, 139], [926, 139], [926, 176], [90, 176]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [90, 139, 926, 176]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 20,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-003'},\n",
       "   {'bboxes': [{'polygon': [[124, 1447],\n",
       "       [324, 1447],\n",
       "       [324, 1518],\n",
       "       [124, 1518]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [124, 1447, 324, 1518]},\n",
       "     {'polygon': [[316, 1479], [336, 1479], [336, 1496], [316, 1496]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [316, 1479, 336, 1496]},\n",
       "     {'polygon': [[126, 139], [792, 139], [792, 226], [126, 226]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [126, 139, 792, 226]},\n",
       "     {'polygon': [[124, 402], [945, 402], [945, 542], [124, 542]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [124, 402, 945, 542]},\n",
       "     {'polygon': [[126, 582], [983, 582], [983, 720], [126, 720]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [126, 582, 983, 720]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 11,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-004'},\n",
       "   {'bboxes': [{'polygon': [[95, 1019], [487, 1019], [487, 1041], [95, 1041]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [95, 1019, 487, 1041]},\n",
       "     {'polygon': [[95, 141], [510, 141], [510, 276], [95, 276]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [95, 141, 510, 276]},\n",
       "     {'polygon': [[547, 162], [852, 162], [852, 275], [547, 275]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [547, 162, 852, 275]},\n",
       "     {'polygon': [[92, 278], [541, 278], [541, 377], [92, 377]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [92, 278, 541, 377]},\n",
       "     {'polygon': [[548, 291], [714, 291], [714, 371], [548, 371]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [548, 291, 714, 371]},\n",
       "     {'polygon': [[94, 944], [763, 944], [763, 1016], [94, 1016]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 944, 763, 1016]},\n",
       "     {'polygon': [[93, 1063], [476, 1063], [476, 1085], [93, 1085]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [93, 1063, 476, 1085]},\n",
       "     {'polygon': [[94, 1109], [966, 1109], [966, 1443], [94, 1443]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 1109, 966, 1443]},\n",
       "     {'polygon': [[95, 1467], [855, 1467], [855, 1511], [95, 1511]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [95, 1467, 855, 1511]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 12,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-005'},\n",
       "   {'bboxes': [{'polygon': [[139, 1510],\n",
       "       [579, 1510],\n",
       "       [579, 1537],\n",
       "       [139, 1537]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Footnote',\n",
       "      'bbox': [139, 1510, 579, 1537]},\n",
       "     {'polygon': [[127, 312], [1003, 312], [1003, 1263], [127, 1263]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 312, 1003, 1263]},\n",
       "     {'polygon': [[128, 1298], [237, 1298], [237, 1323], [128, 1323]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 1298, 237, 1323]},\n",
       "     {'polygon': [[816, 1299], [1001, 1299], [1001, 1414], [816, 1414]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [816, 1299, 1001, 1414]},\n",
       "     {'polygon': [[504, 145], [625, 145], [625, 179], [504, 179]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Title',\n",
       "      'bbox': [504, 145, 625, 179]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 9,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-006'},\n",
       "   {'bboxes': [{'polygon': [[128, 313], [333, 313], [333, 344], [128, 344]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [128, 313, 333, 344]},\n",
       "     {'polygon': [[128, 597], [404, 597], [404, 627], [128, 627]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [128, 597, 404, 627]},\n",
       "     {'polygon': [[128, 757], [344, 757], [344, 787], [128, 787]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [128, 757, 344, 787]},\n",
       "     {'polygon': [[129, 948], [338, 948], [338, 978], [129, 978]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [129, 948, 338, 978]},\n",
       "     {'polygon': [[128, 1202], [398, 1202], [398, 1232], [128, 1232]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [128, 1202, 398, 1232]},\n",
       "     {'polygon': [[127, 1012], [456, 1012], [456, 1174], [127, 1174]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [127, 1012, 456, 1174]},\n",
       "     {'polygon': [[489, 1011], [918, 1011], [918, 1178], [489, 1178]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [489, 1011, 918, 1178]},\n",
       "     {'polygon': [[125, 1247], [883, 1247], [883, 1533], [125, 1533]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [125, 1247, 883, 1533]},\n",
       "     {'polygon': [[493, 370], [999, 370], [999, 553], [493, 553]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [493, 370, 999, 553]},\n",
       "     {'polygon': [[128, 370], [306, 370], [306, 396], [128, 396]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 370, 306, 396]},\n",
       "     {'polygon': [[128, 433], [261, 433], [261, 459], [128, 459]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 433, 261, 459]},\n",
       "     {'polygon': [[128, 495], [314, 495], [314, 553], [128, 553]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 495, 314, 553]},\n",
       "     {'polygon': [[493, 660], [906, 660], [906, 719], [493, 719]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [493, 660, 906, 719]},\n",
       "     {'polygon': [[128, 660], [302, 660], [302, 719], [128, 719]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 660, 302, 719]},\n",
       "     {'polygon': [[128, 820], [286, 820], [286, 910], [128, 910]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 820, 286, 910]},\n",
       "     {'polygon': [[493, 819], [906, 819], [906, 911], [493, 911]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [493, 819, 906, 911]},\n",
       "     {'polygon': [[462, 143], [668, 143], [668, 178], [462, 178]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Title',\n",
       "      'bbox': [462, 143, 668, 178]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 6,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-007'},\n",
       "   {'bboxes': [{'polygon': [[174, 75], [299, 75], [299, 98], [174, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [174, 75, 299, 98]},\n",
       "     {'polygon': [[95, 75], [130, 75], [130, 98], [95, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [95, 75, 130, 98]},\n",
       "     {'polygon': [[82, 113], [968, 113], [968, 966], [82, 966]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [82, 113, 968, 966]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 3,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-008'},\n",
       "   {'bboxes': [{'polygon': [[128, 311], [466, 311], [466, 337], [128, 337]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [128, 311, 466, 337]},\n",
       "     {'polygon': [[128, 946], [476, 946], [476, 973], [128, 973]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [128, 946, 476, 973]},\n",
       "     {'polygon': [[128, 1224], [342, 1224], [342, 1249], [128, 1249]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [128, 1224, 342, 1249]},\n",
       "     {'polygon': [[127, 364], [943, 364], [943, 510], [127, 510]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 364, 943, 510]},\n",
       "     {'polygon': [[128, 544], [945, 544], [945, 629], [128, 629]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 544, 945, 629]},\n",
       "     {'polygon': [[127, 661], [944, 661], [944, 748], [127, 748]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 661, 944, 748]},\n",
       "     {'polygon': [[128, 782], [941, 782], [941, 899], [128, 899]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 782, 941, 899]},\n",
       "     {'polygon': [[128, 1000], [940, 1000], [940, 1056], [128, 1056]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 1000, 940, 1056]},\n",
       "     {'polygon': [[128, 1089], [940, 1089], [940, 1174], [128, 1174]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 1089, 940, 1174]},\n",
       "     {'polygon': [[128, 1276], [942, 1276], [942, 1362], [128, 1362]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 1276, 942, 1362]},\n",
       "     {'polygon': [[128, 1395], [1000, 1395], [1000, 1452], [128, 1452]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 1395, 1000, 1452]},\n",
       "     {'polygon': [[494, 145], [637, 145], [637, 178], [494, 178]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Title',\n",
       "      'bbox': [494, 145, 637, 178]},\n",
       "     {'polygon': [[980, 396], [1002, 396], [1002, 418], [980, 418]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [980, 396, 1002, 418]},\n",
       "     {'polygon': [[969, 576], [1000, 576], [1000, 598], [969, 598]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [969, 576, 1000, 598]},\n",
       "     {'polygon': [[968, 695], [1001, 695], [1001, 718], [968, 718]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [968, 695, 1001, 718]},\n",
       "     {'polygon': [[966, 815], [1001, 815], [1001, 838], [966, 838]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [966, 815, 1001, 838]},\n",
       "     {'polygon': [[968, 1002], [1001, 1002], [1001, 1025], [968, 1025]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [968, 1002, 1001, 1025]},\n",
       "     {'polygon': [[966, 1120], [1001, 1120], [1001, 1143], [966, 1143]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [966, 1120, 1001, 1143]},\n",
       "     {'polygon': [[956, 1309], [999, 1309], [999, 1331], [956, 1331]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [956, 1309, 999, 1331]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 5,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-009'},\n",
       "   {'bboxes': [{'polygon': [[174, 76], [262, 76], [262, 98], [174, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [174, 76, 262, 98]},\n",
       "     {'polygon': [[94, 242], [389, 242], [389, 268], [94, 268]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [94, 242, 389, 268]},\n",
       "     {'polygon': [[94, 548], [227, 548], [227, 575], [94, 575]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [94, 548, 227, 575]},\n",
       "     {'polygon': [[94, 137], [965, 137], [965, 193], [94, 193]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 137, 965, 193]},\n",
       "     {'polygon': [[94, 293], [965, 293], [965, 350], [94, 350]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 293, 965, 350]},\n",
       "     {'polygon': [[119, 354], [620, 354], [620, 381], [119, 381]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [119, 354, 620, 381]},\n",
       "     {'polygon': [[93, 413], [918, 413], [918, 499], [93, 499]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [93, 413, 918, 499]},\n",
       "     {'polygon': [[914, 446], [965, 446], [965, 468], [914, 468]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [914, 446, 965, 468]},\n",
       "     {'polygon': [[94, 601], [917, 601], [917, 657], [94, 657]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 601, 917, 657]},\n",
       "     {'polygon': [[915, 603], [965, 603], [965, 625], [915, 625]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [915, 603, 965, 625]},\n",
       "     {'polygon': [[93, 689], [916, 689], [916, 776], [93, 776]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [93, 689, 916, 776]},\n",
       "     {'polygon': [[919, 721], [965, 721], [965, 745], [919, 745]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [919, 721, 965, 745]},\n",
       "     {'polygon': [[94, 809], [963, 809], [963, 866], [94, 866]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 809, 963, 866]},\n",
       "     {'polygon': [[118, 870], [811, 870], [811, 897], [118, 897]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [118, 870, 811, 897]},\n",
       "     {'polygon': [[93, 76], [114, 76], [114, 98], [93, 98]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [93, 76, 114, 98]},\n",
       "     {'polygon': [[94, 926], [915, 926], [915, 956], [94, 956]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 926, 915, 956]},\n",
       "     {'polygon': [[919, 932], [965, 932], [965, 955], [919, 955]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [919, 932, 965, 955]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 10,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-010'},\n",
       "   {'bboxes': [{'polygon': [[226, 798], [903, 798], [903, 863], [226, 863]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [226, 798, 903, 863]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 13,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-011'},\n",
       "   {'bboxes': [{'polygon': [[129, 1456],\n",
       "       [1000, 1456],\n",
       "       [1000, 1505],\n",
       "       [129, 1505]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Footnote',\n",
       "      'bbox': [129, 1456, 1000, 1505]},\n",
       "     {'polygon': [[129, 1519], [858, 1519], [858, 1580], [129, 1580]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Footnote',\n",
       "      'bbox': [129, 1519, 858, 1580]},\n",
       "     {'polygon': [[956, 45], [1049, 45], [1049, 134], [956, 134]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [956, 45, 1049, 134]},\n",
       "     {'polygon': [[153, 321], [968, 321], [968, 407], [153, 407]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [153, 321, 968, 407]},\n",
       "     {'polygon': [[197, 436], [902, 436], [902, 657], [197, 657]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [197, 436, 902, 657]},\n",
       "     {'polygon': [[200, 727], [930, 727], [930, 1435], [200, 1435]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [200, 727, 930, 1435]},\n",
       "     {'polygon': [[233, 131], [896, 131], [896, 254], [233, 254]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Title',\n",
       "      'bbox': [233, 131, 896, 254]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 8,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-012'},\n",
       "   {'bboxes': [{'polygon': [[174, 78], [334, 78], [334, 99], [174, 99]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [174, 78, 334, 99]},\n",
       "     {'polygon': [[94, 77], [113, 77], [113, 98], [94, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [94, 77, 113, 98]},\n",
       "     {'polygon': [[95, 469], [335, 469], [335, 497], [95, 497]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [95, 469, 335, 497]},\n",
       "     {'polygon': [[164, 142], [894, 142], [894, 301], [164, 301]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [164, 142, 894, 301]},\n",
       "     {'polygon': [[164, 347], [852, 347], [852, 423], [164, 423]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [164, 347, 852, 423]},\n",
       "     {'polygon': [[94, 528], [965, 528], [965, 1507], [94, 1507]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 528, 965, 1507]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 7,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-013'},\n",
       "   {'bboxes': [{'polygon': [[421, 76], [917, 76], [917, 99], [421, 99]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [421, 76, 917, 99]},\n",
       "     {'polygon': [[980, 75], [1001, 75], [1001, 98], [980, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [980, 75, 1001, 98]},\n",
       "     {'polygon': [[127, 140], [1003, 140], [1003, 1510], [127, 1510]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 140, 1003, 1510]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 16,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-014'},\n",
       "   {'bboxes': [{'polygon': [[96, 583], [965, 583], [965, 716], [96, 716]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Caption',\n",
       "      'bbox': [96, 583, 965, 716]},\n",
       "     {'polygon': [[100, 1452], [964, 1452], [964, 1506], [100, 1506]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Footnote',\n",
       "      'bbox': [100, 1452, 964, 1506]},\n",
       "     {'polygon': [[493, 1297], [597, 1297], [597, 1362], [493, 1362]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Formula',\n",
       "      'bbox': [493, 1297, 597, 1362]},\n",
       "     {'polygon': [[176, 77], [331, 77], [331, 96], [176, 96]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [176, 77, 331, 96]},\n",
       "     {'polygon': [[94, 77], [110, 77], [110, 96], [94, 96]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [94, 77, 110, 96]},\n",
       "     {'polygon': [[162, 114], [896, 114], [896, 569], [162, 569]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [162, 114, 896, 569]},\n",
       "     {'polygon': [[94, 767], [488, 767], [488, 795], [94, 795]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [94, 767, 488, 795]},\n",
       "     {'polygon': [[94, 828], [965, 828], [965, 1209], [94, 1209]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 828, 965, 1209]},\n",
       "     {'polygon': [[99, 1228], [965, 1228], [965, 1284], [99, 1284]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [99, 1228, 965, 1284]},\n",
       "     {'polygon': [[99, 1381], [965, 1381], [965, 1435], [99, 1435]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [99, 1381, 965, 1435]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 14,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-015'},\n",
       "   {'bboxes': [{'polygon': [[301, 330], [853, 330], [853, 435], [301, 435]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Formula',\n",
       "      'bbox': [301, 330, 853, 435]},\n",
       "     {'polygon': [[276, 707], [882, 707], [882, 798], [276, 798]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Formula',\n",
       "      'bbox': [276, 707, 882, 798]},\n",
       "     {'polygon': [[422, 857], [740, 857], [740, 944], [422, 944]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Formula',\n",
       "      'bbox': [422, 857, 740, 944]},\n",
       "     {'polygon': [[414, 1060], [739, 1060], [739, 1149], [414, 1149]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Formula',\n",
       "      'bbox': [414, 1060, 739, 1149]},\n",
       "     {'polygon': [[421, 76], [917, 76], [917, 99], [421, 99]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [421, 76, 917, 99]},\n",
       "     {'polygon': [[181, 141], [623, 141], [623, 164], [181, 164]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [181, 141, 623, 164]},\n",
       "     {'polygon': [[180, 171], [606, 171], [606, 194], [180, 194]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [180, 171, 606, 194]},\n",
       "     {'polygon': [[180, 200], [803, 200], [803, 222], [180, 222]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [180, 200, 803, 222]},\n",
       "     {'polygon': [[136, 230], [1000, 230], [1000, 313], [136, 313]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [136, 230, 1000, 313]},\n",
       "     {'polygon': [[135, 455], [1001, 455], [1001, 688], [135, 688]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [135, 455, 1001, 688]},\n",
       "     {'polygon': [[135, 814], [1000, 814], [1000, 871], [135, 871]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [135, 814, 1000, 871]},\n",
       "     {'polygon': [[151, 957], [1000, 957], [1000, 1074], [151, 1074]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [151, 957, 1000, 1074]},\n",
       "     {'polygon': [[127, 1171], [1001, 1171], [1001, 1462], [127, 1462]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [127, 1171, 1001, 1462]},\n",
       "     {'polygon': [[982, 75], [999, 75], [999, 96], [982, 96]],\n",
       "      'confidence': 0.5,\n",
       "      'label': 'Text',\n",
       "      'bbox': [982, 75, 999, 96]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 18,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-016'},\n",
       "   {'bboxes': [{'polygon': [[95, 876], [966, 876], [966, 955], [95, 955]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Caption',\n",
       "      'bbox': [95, 876, 966, 955]},\n",
       "     {'polygon': [[176, 76], [334, 76], [334, 99], [176, 99]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [176, 76, 334, 99]},\n",
       "     {'polygon': [[94, 75], [111, 75], [111, 98], [94, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [94, 75, 111, 98]},\n",
       "     {'polygon': [[203, 416], [854, 416], [854, 862], [203, 862]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [203, 416, 854, 862]},\n",
       "     {'polygon': [[94, 136], [446, 136], [446, 164], [94, 164]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [94, 136, 446, 164]},\n",
       "     {'polygon': [[95, 334], [778, 334], [778, 358], [95, 358]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [95, 334, 778, 358]},\n",
       "     {'polygon': [[94, 199], [965, 199], [965, 283], [94, 283]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 199, 965, 283]},\n",
       "     {'polygon': [[93, 1015], [966, 1015], [966, 1487], [93, 1487]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [93, 1015, 966, 1487]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 19,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-017'},\n",
       "   {'bboxes': [{'polygon': [[129, 1246],\n",
       "       [999, 1246],\n",
       "       [999, 1350],\n",
       "       [129, 1350]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Caption',\n",
       "      'bbox': [129, 1246, 999, 1350]},\n",
       "     {'polygon': [[421, 75], [918, 75], [918, 98], [421, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [421, 75, 918, 98]},\n",
       "     {'polygon': [[980, 76], [1000, 76], [1000, 95], [980, 95]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [980, 76, 1000, 95]},\n",
       "     {'polygon': [[123, 913], [1005, 913], [1005, 1227], [123, 1227]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [123, 913, 1005, 1227]},\n",
       "     {'polygon': [[194, 136], [956, 136], [956, 162], [194, 162]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [194, 136, 956, 162]},\n",
       "     {'polygon': [[129, 139], [174, 139], [174, 163], [129, 163]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [129, 139, 174, 163]},\n",
       "     {'polygon': [[129, 830], [799, 830], [799, 855], [129, 855]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [129, 830, 799, 855]},\n",
       "     {'polygon': [[128, 186], [1004, 186], [1004, 779], [128, 779]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 186, 1004, 779]},\n",
       "     {'polygon': [[129, 1413], [1000, 1413], [1000, 1497], [129, 1497]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [129, 1413, 1000, 1497]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 2,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-018'},\n",
       "   {'bboxes': [{'polygon': [[409, 571], [644, 571], [644, 653], [409, 653]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Formula',\n",
       "      'bbox': [409, 571, 644, 653]},\n",
       "     {'polygon': [[174, 77], [334, 77], [334, 99], [174, 99]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [174, 77, 334, 99]},\n",
       "     {'polygon': [[94, 76], [124, 76], [124, 98], [94, 98]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [94, 76, 124, 98]},\n",
       "     {'polygon': [[95, 707], [258, 707], [258, 733], [95, 733]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [95, 707, 258, 733]},\n",
       "     {'polygon': [[95, 768], [778, 768], [778, 792], [95, 792]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Section-header',\n",
       "      'bbox': [95, 768, 778, 792]},\n",
       "     {'polygon': [[227, 929], [837, 929], [837, 1269], [227, 1269]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [227, 929, 837, 1269]},\n",
       "     {'polygon': [[94, 141], [966, 141], [966, 581], [94, 581]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [94, 141, 966, 581]},\n",
       "     {'polygon': [[95, 867], [964, 867], [964, 917], [95, 917]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [95, 867, 964, 917]},\n",
       "     {'polygon': [[93, 1325], [966, 1325], [966, 1500], [93, 1500]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [93, 1325, 966, 1500]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 1,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-019'},\n",
       "   {'bboxes': [{'polygon': [[129, 868], [1002, 868], [1002, 973], [129, 973]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Caption',\n",
       "      'bbox': [129, 868, 1002, 973]},\n",
       "     {'polygon': [[422, 76], [917, 76], [917, 99], [422, 99]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [422, 76, 917, 99]},\n",
       "     {'polygon': [[970, 76], [1000, 76], [1000, 97], [970, 97]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Page-header',\n",
       "      'bbox': [970, 76, 1000, 97]},\n",
       "     {'polygon': [[119, 190], [974, 197], [968, 860], [113, 854]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Figure',\n",
       "      'bbox': [119, 190, 974, 860]},\n",
       "     {'polygon': [[204, 131], [979, 131], [979, 192], [204, 192]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Table',\n",
       "      'bbox': [204, 131, 979, 192]},\n",
       "     {'polygon': [[128, 1029], [1002, 1029], [1002, 1384], [128, 1384]],\n",
       "      'confidence': 1.0,\n",
       "      'label': 'Text',\n",
       "      'bbox': [128, 1029, 1002, 1384]}],\n",
       "    'image_bbox': [0.0, 0.0, 1099.0, 1666.0],\n",
       "    'page': 4,\n",
       "    'name': 'f753e82b-5737-46d7-996c-14dd7ad2efa8-020'}]}}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_surya_layout_pdf_data.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['text', 'images', 'attached_images', 'attached_images_map', 'metadata'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_surya_layout_pdf_data.to_dict().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/zephyr/Desktop/Wisup/code/wisup_project/e2m/figure/16.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 让我们看看 images 的部分\n",
    "# 你会发现页眉部分被遮盖了，并且图片部分被正确识别出来了\n",
    "from IPython.display import Image, display\n",
    "\n",
    "image_16 = test_surya_layout_pdf_data.images[16]\n",
    "print(image_16)\n",
    "\n",
    "display(Image(image_16))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### 文本类型\n",
      "本书籍属于学术论文集或会议论文集 (\"workshop proceedings\"). \n",
      "\n",
      "### 文本格式\n",
      "- 书籍封面和内页说明书籍的标题、编辑和出版信息，常见于学术出版物。\n",
      "- 目录显示了清晰的章节和专题分类, 用蓝色字体区分了主体内容。\n",
      "\n",
      "### 标题格式\n",
      "1. 封面\n",
      "   - **书名**: Explainable and Transparent AI and Multi-Agent Systems\n",
      "   - **会议信息**: 4th International Workshop, EXTRAAAMAS 2022\n",
      "   - **编辑信息**: Davide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling\n",
      "   - **出版商**: Springer\n",
      "2. 版权页面\n",
      "   - **版权声明和 ISBN 编号**: ISSN, ISBN, DOI 链接\n",
      "3. 前言\n",
      "   - 标题: **Preface**\n",
      "4. 组织机构\n",
      "   - 标题: **Organization**\n",
      "   - 各职位和成员: General Chairs, Special Track Chairs, Publicity Chairs, Advisory Board, Program Committee\n",
      "\n",
      "5. 目录\n",
      "   - 一级标题: \n",
      "     - Explainable Machine Learning\n",
      "     - Explainable Neuro-Symbolic AI\n",
      "     - Explainable Agents\n",
      "     - XAI Measures and Metrics\n",
      "     - AI and Law\n",
      "   - 内容的细分显示了文章标题和页码。\n",
      "\n",
      "示例：\n",
      "- 一级标题\n",
      "  ```\n",
      "  # Explainable Machine Learning\n",
      "  ## Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography\n",
      "  # Explainable Neuro-Symbolic AI\n",
      "  ## Recent Neural-Symbolic Approaches to ILP Based on Templates\n",
      "  ```\n",
      "\n",
      "### 特殊标注\n",
      "- 如果一些小标题不符合常规结构，如“Preface”和“Organization”中的各个子标题，用**加粗**来标记。\n",
      "   - **General Chairs**\n",
      "   - **Special Track Chairs**\n",
      "   - **Publicity Chairs**\n",
      "   - **Advisory Board**\n",
      "   - **Program Committee**\n",
      "\n",
      "### 推断\n",
      "通过以上信息判断，这是一部学术会议论文集，采用的是论文集常见的结构和格式。None# Explainable and Transparent AI and Multi-Agent Systems\n",
      "\n",
      "4th International Workshop, EXTRAAMAS 2022  \n",
      "Virtual Event, May 9–10, 2022  \n",
      "Revised Selected Papers\n",
      "\n",
      "![](figure/0_0.png)\n",
      "\n",
      "---\n",
      "\n",
      "**Lecture Notes in Artificial Intelligence** 13283\n",
      "\n",
      "Subseries of Lecture Notes in Computer Science\n",
      "\n",
      "Series Editors  \n",
      "Randy Goebel  \n",
      "*University of Alberta, Edmonton, Canada*  \n",
      "Wolfgang Wahlster  \n",
      "*DFKI, Berlin, Germany*  \n",
      "Zhi-Hua Zhou  \n",
      "*Nanjing University, Nanjing, China*\n",
      "\n",
      "Founding Editor  \n",
      "Jörg Siekmann  \n",
      "*DFKI and Saarland University, Saarbrücken, Germany*  \n",
      "<NEWLINE>\n",
      "\n",
      "---\n",
      "\n",
      "More information about this subseries at [https://link.springer.com/bookseries/1244](https://link.springer.com/bookseries/1244)  \n",
      "<NEWLINE>\n",
      "\n",
      "---\n",
      "\n",
      "Davide Calvaresi · Amro Najjar ·  \n",
      "Michael Winikoff · Kary Främling (Eds.)\n",
      "\n",
      "**Explainable and Transparent  \n",
      "AI and Multi-Agent Systems**\n",
      "\n",
      "4th International Workshop, EXTRAAMAS 2022  \n",
      "Virtual Event, May 9–10, 2022  \n",
      "Revised Selected Papers\n",
      "\n",
      "![](figure/0_0.png)\n",
      "\n",
      "---\n",
      "\n",
      "Editors  \n",
      "Davide Calvaresi  \n",
      "*University of Applied Sciences and Arts  \n",
      "of Western Switzerland  \n",
      "Sierre, Switzerland*\n",
      "\n",
      "Amro Najjar  \n",
      "*Luxembourg Institute of Science and Technology  \n",
      "Esch-sur-Alzette, Luxembourg*\n",
      "\n",
      "Michael Winikoff  \n",
      "*Victoria University of Wellington  \n",
      "Wellington, New Zealand*\n",
      "\n",
      "Kary Främling  \n",
      "*Umeå University  \n",
      "Umeå, Sweden*\n",
      "\n",
      "---\n",
      "\n",
      "ISSN 0302-9743 ISSN 1611-3349 (electronic)  \n",
      "Lecture Notes in Artificial Intelligence  \n",
      "ISBN 978-3-031-15564-2 ISBN 978-3-031-15565-9 (eBook)  \n",
      "[https://doi.org/10.1007/978-3-031-15565-9](https://doi.org/10.1007/978-3-031-15565-9)  \n",
      "\n",
      "LNCS Sublibrary: SL7 – Artificial Intelligence\n",
      "\n",
      "© The Editor(s) (if applicable) and The Author(s), under exclusive license  \n",
      "to Springer Nature Switzerland AG 2022  \n",
      "This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.  \n",
      "The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.  \n",
      "The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\n",
      "\n",
      "This Springer imprint is published by the registered company Springer Nature Switzerland AG  \n",
      "The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland  \n",
      "<NEWLINE>None**Preface**\n",
      "\n",
      "The increasingly complex intelligent agents/robots rely on often-opaque machine learning-based algorithms. Explaining such algorithms is a chief priority to enhance their acceptability, avoid failures, foster trust, and comply with relevant (inter)national regulations. The eXplainable Artificial Intelligence (XAI) domain aims to generate new methods to explain and interpret the reasoning and results of such systems.\n",
      "\n",
      "The 2022 edition of the EXplainable and TRansparent AI and Multi-Agent Systems (EXTRAAMAS) continued the successful track initiated in 2019 at Montreal and followed by the 2020 edition in New Zealand (virtual due to the COVID-19 pandemic circumstances) and the virtual conference in 2021. For the 2022 edition, the aim was again to have it in New Zealand, which was again prevented by the pandemic circumstances. Despite these challenges, EXTRAAMAS 2022 can be considered a success. EXTRAAMAS 2022 set the following aims: (i) to strengthen the common ground for the study and development of explainable and understandable autonomous agents, robots, and multi-agent systems (MAS), (ii) to investigate the potential of agent-based systems in the development of personalized user-aware explainable AI, (iii) to assess the impact of transparent and explained solutions on the user/agents behaviors, (iv) to discuss motivating examples and concrete applications in which the lack of explainability leads to problems, which would be resolved by explainability, and (v) to assess and discuss the first demonstrators and proof of concepts paving the way for the next generation systems. EXTRAAMAS 2022 received 25 submissions. Each submission underwent a rigorous single-blind peer-review process (three to five reviews per paper). Eventually, 14 papers were accepted—contained in this volume. For each paper, the authors performed live video presentations that, with their consent, are available on the EXTRAAMAS website Moreover, EXTRAAMAS 2022 proposed two keynotes: “From Explanation to Justification to Trust in Human-Machine Interac-tion” given by Bertram Malle from Brown University, and “Towards Natural Language Explanatory Argument Generation: Achieved Results and Open Challenges” given by Serena Villata from CNRS, France.\n",
      "\n",
      "We would like to thank the special track chairs, publicity chairs, and Program Committee for their valuable work. We also thank the authors, presenters, and participants. Particular emphasis goes to Bertram Malle and Serena Villata for their fantastic keynotes.\n",
      "\n",
      "May 2022\n",
      "\n",
      "Davide Calvaresi  \n",
      "Amro Najjar  \n",
      "Michael Winikoff  \n",
      "Kary Främling\n",
      "\n",
      "1 https://extraamas.ehealth.hevs.ch/archive.html.  \n",
      "<NEWLINE>\n",
      "\n",
      "**Organization**\n",
      "\n",
      "**General Chairs**\n",
      "\n",
      "Davide Calvaresi  \n",
      "University of Applied Sciences Western Switzerland, Switzerland  \n",
      "\n",
      "Amro Najjar  \n",
      "Luxembourg Institute of Science and Technology, Luxembourg  \n",
      "\n",
      "Michael Winikoff  \n",
      "Victoria University of Wellington, New Zealand  \n",
      "\n",
      "Kary Främling  \n",
      "Umeå University, Sweden  \n",
      "\n",
      "**Special Track Chairs**\n",
      "\n",
      "Réka Markovich  \n",
      "University of Luxembourg, Luxembourg  \n",
      "\n",
      "Giovanni Ciatto  \n",
      "University of Bologna, Italy  \n",
      "\n",
      "**Publicity Chairs**\n",
      "\n",
      "Yazan Mualla  \n",
      "UTBM, France  \n",
      "\n",
      "Benoit Alcaraz  \n",
      "University of Luxembourg, Luxembourg  \n",
      "\n",
      "Rachele Carli  \n",
      "University of Bologna, Italy  \n",
      "\n",
      "**Advisory Board**\n",
      "\n",
      "Tim Miller  \n",
      "University of Melbourne, Australia  \n",
      "\n",
      "Michael Schumacher  \n",
      "University of Applied Sciences Western Switzerland, Switzerland  \n",
      "\n",
      "Virginia Dignum  \n",
      "Umeå University, Sweden  \n",
      "\n",
      "Leon van der Torre  \n",
      "University of Luxembourg, Luxembourg  \n",
      "\n",
      "**Program Committee**\n",
      "\n",
      "Andrea Agiollo  \n",
      "University of Bologna, Italy  \n",
      "\n",
      "Natasha Alechina  \n",
      "Utrecht University, The Netherlands  \n",
      "\n",
      "Michał Araszkiewicz  \n",
      "Jagiellonian University, Poland  \n",
      "\n",
      "Katie Atkinson  \n",
      "University of Liverpool, UK  \n",
      "\n",
      "Kim Baraka  \n",
      "VU Amsterdam, The Netherlands  \n",
      "\n",
      "Jamal Barafi  \n",
      "Al Ain University, UAE  \n",
      "\n",
      "Suna Bensch  \n",
      "Umeå University, Sweden  \n",
      "\n",
      "Remy Chaput  \n",
      "University of Lyon 1, France  \n",
      "\n",
      "Victor Contreras  \n",
      "HES-SO Valais-Wallis, Switzerland  \n",
      "\n",
      "Alaa Daoud  \n",
      "Mines Saint-Étienne, France  \n",
      "\n",
      "Stephane Galland  \n",
      "UTBM, France  \n",
      "\n",
      "Brent Harrison  \n",
      "Georgia Institute of Technology, USA  \n",
      "\n",
      "Thomas Hellström  \n",
      "Umeå University, Sweden  \n",
      "\n",
      "Timotheus Kampik  \n",
      "University of Umea and SAP Signavio, Sweden  \n",
      "\n",
      "Isaac Lage  \n",
      "Harvard University, USA  \n",
      "\n",
      "Christopher Leturc  \n",
      "Inria Sophia Antipolis, France  \n",
      "\n",
      "Beishui Liao  \n",
      "Zhejiang University, China  \n",
      "\n",
      "Tomer Libal  \n",
      "University of Luxembourg, Luxembourg  \n",
      "\n",
      "Matteo Magnini  \n",
      "University of Bologna, Italy  \n",
      "\n",
      "Viviana Mascardi  \n",
      "University of Genoa, Italy  \n",
      "\n",
      "Laëtitia Matignon  \n",
      "University of Lyon 1, France  \n",
      "\n",
      "Monica Palmirani  \n",
      "University of Bologna, Italy  \n",
      "\n",
      "Gauthier Picard  \n",
      "Mines Saint-Étienne, France  \n",
      "\n",
      "Francisco Rodríguez Lera  \n",
      "University of León, Spain  \n",
      "\n",
      "Federico Sabbatini  \n",
      "University of Bologna, Italy  \n",
      "\n",
      "Paolo Serrani  \n",
      "Università Politecnica delle Marche, Italy  \n",
      "\n",
      "Sarath Sreedharan  \n",
      "Arizona State University, USA  \n",
      "\n",
      "Igor Tchappi  \n",
      "University of Luxembourg, Luxembourg  \n",
      "\n",
      "Bart Verheij  \n",
      "University of Groningen, The Netherlands  \n",
      "\n",
      "Robert Wortham  \n",
      "University of Bath, UK  \n",
      "\n",
      "Deshraj Yadav  \n",
      "Georgia Institute of Technology, USA  \n",
      "\n",
      "Jessie Yang  \n",
      "University of Michigan, USA  \n",
      "<NEWLINE>\n",
      "\n",
      "**Contents**\n",
      "\n",
      "**Explainable Machine Learning**\n",
      "\n",
      "Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography ............................................... 3  \n",
      "Lennart Brocki, Wistan Marchadour, Jonas Maison, Bogdan Badic,  \n",
      "Panagiotis Papadimitroulas, Mathieu Hatt, Franck Vermet,  \n",
      "and Neo Christopher Chung  \n",
      "\n",
      "Integration of Local and Global Features Explanation with Global Rules Extraction and Generation Tools ...................................... 19  \n",
      "Victor Contreras, Michael Schumacher, and Davide Calvaresi  \n",
      "\n",
      "ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning ........................ 38  \n",
      "Jasmina Gajcin and Ivana Dusparic  \n",
      "\n",
      "Smartphone Based Grape Leaf Disease Diagnosis and Remedial System Assisted with Explanations ............... 57  \n",
      "Avleen Malhi, Vlad Aoppei, Manik Madhikermi, Mandeep,  \n",
      "and Kary Främling  \n",
      "\n",
      "**Explainable Neuro-Symbolic AI**\n",
      "\n",
      "Recent Neural-Symbolic Approaches to ILP Based on Templates ....... 75  \n",
      "Davide Beretta, Stefania Monica, and Federico Bergenti  \n",
      "\n",
      "On the Design of PSyKI: A Platform for Symbolic Knowledge Injection into Sub-symbolic Predictors ......... 90  \n",
      "Matteo Magnini, Giovanni Ciatto, and Andrea Omicini  \n",
      "\n",
      "**Explainable Agents**\n",
      "\n",
      "The Mirror Agent Model: A Bayesian Architecture for Interpretable Agent Behavior ............................ 111  \n",
      "Michele Persiani and Thomas Hellström  \n",
      "\n",
      "Semantic Web-Based Interoperability for Intelligent Agents with PSyKE ..... 124  \n",
      "Federico Sabbatini, Giovanni Ciatto, and Andrea Omicini  \n",
      "\n",
      "Case-Based Reasoning via Comparing the Strength Order of Features ....... 143  \n",
      "Liuwen Yu and Dov Gabbay  \n",
      "\n",
      "**XAI Measures and Metrics**\n",
      "\n",
      "Explainability Metrics and Properties for Counterfactual Explanation Methods ................................................. 155  \n",
      "Vandita Singh, Kristijonas Cyras, and Rafia Inam  \n",
      "\n",
      "The Use of Partial Order Relations and Measure Theory in Developing Objective Measures of Explainability ........................... 173  \n",
      "Wim De Mulder  \n",
      "\n",
      "**AI and Law**\n",
      "\n",
      "An Evaluation of Methodologies for Legal Formalization .............. 189  \n",
      "Tereza Novotná and Tomer Libal  \n",
      "\n",
      "Risk and Exposure of XAI in Persuasion and Argumentation: The case of Manipulation ............................................. 204  \n",
      "Rachele Carli, Amro Najjar, and Davide Calvaresi  \n",
      "\n",
      "Requirements for Tax XAI Under Constitutional Principles and Human Rights .............................................. 221  \n",
      "Błażej Kuzniacki, Marco Almada, Kamil Tyliński, and Łukasz Górski  \n",
      "\n",
      "Author Index .............................................. 239  \n",
      "<NEWLINE>None# Explainable Machine Learning\n",
      "\n",
      "## Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography\n",
      "\n",
      "Lennart Brocki\\(1\\), Wistan Marchadour\\(2,3\\), Jonas Maison\\(2,4\\), Bogdan Badic\\(2\\), Panagiotis Papadimitroulas\\(5\\), Mathieu Hatt\\(2\\) \\(\\textit{✉}\\), Franck Vermet\\(3\\) \\(\\textit{✉}\\), and Neo Christopher Chung\\(1\\) \\(\\textit{✉}\\)\n",
      "\n",
      "1. Institute of Informatics, University of Warsaw, Warsaw, Poland  \n",
      "   nchchung@gmail.com  \n",
      "2. LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France  \n",
      "   hatt@univ-brest.fr  \n",
      "3. LMBA, CNRS, UMR 6205, Univ Brest, Brest, France  \n",
      "   Franck.Vermet@univ-brest.fr  \n",
      "4. Aquilab, Lille, France  \n",
      "5. Bioemission Technology Solutions - BIOEMTECH, Athens, Greece  \n",
      "\n",
      "### Abstract\n",
      "Deep learning has shown superb performance in detecting objects and classifying images, ensuring a great promise for analyzing medical imaging. Translating the success of deep learning to medical imaging, in which doctors need to understand the underlying process, requires the capability to interpret and explain the prediction of neural networks. Interpretability of deep neural networks often relies on estimating the importance of input features (e.g., pixels) with respect to the outcome (e.g., class probability). However, a number of importance estimators (also known as saliency maps) have been developed and it is unclear which ones are more relevant for medical imaging applications. In the present work, we investigated the performance of several importance estimators in explaining the classification of computed tomography (CT) images by a convolutional deep network, using three distinct evaluation metrics. Specifically, the ResNet-50 was trained to classify CT scans of lungs acquired with and without contrast agents, in which clinically relevant anatomical areas were manually determined by experts as segmentation masks in the images. Three evaluation metrics were used to quantify different aspects of interpretability. First, the model-centric fidelity measures a decrease in the model accuracy when certain inputs are perturbed. Second, concordance between importance scores and the expert-defined segmentation masks is measured on a pixel level by a receiver operating characteristic (ROC) curves. Third, we measure a region-wise overlap between a XRAI-based map and the segmentation mask by Dice Similarity Coefficients (DSC). Overall, two versions of SmoothGrad topped the fidelity and ROC rankings, whereas both Integrated Gradients and SmoothGrad excelled in DSC evaluation.  \n",
      "\n",
      "L. Brocki, W. Marchadour, M. Hatt, F. Vermet and N. C. Chung—These authors contributed equally.  \n",
      "\n",
      "© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022  \n",
      "D. Calvaresi et al. (Eds.): EXTRAAAMAS 2022, LNAI 13283, pp. 3–18, 2022.  \n",
      "https://doi.org/10.1007/978-3-031-15565-9_1  \n",
      "\n",
      "Interestingly, there was a critical discrepancy between model-centric (fidelity) and human-centric (ROC and DSC) evaluation. Expert expectation and intuition embedded in segmentation maps does not necessarily align with how the model arrived at its prediction. Understanding this difference in interpretability would help harnessing the power of deep learning in medicine.\n",
      "\n",
      "**Keywords**:  \n",
      "Deep learning · Neural network · Medical imaging · Computed tomography · Interpretability · Explainability · Saliency map\n",
      "\n",
      "1. **Introduction**\n",
      "\n",
      "Deep learning models have shown high performance in a plethora of computer vision tasks in the last decade. This impressive performance, however, comes at the cost of lacking transparency, explainability and interpretability of their decision making process [22]. This is particularly problematic in safety-sensitive fields such as health care [18,25], as due to the end-to-end architecture employed, a model’s behavior is usually not fully testable and it can fail in unexpected ways [9]. In medical image analysis, it is therefore beneficial to consider interpretability methods that can quantify the relative importance of input features with respect to the class probability, and thus allow to obtain a better understanding of the model’s decisions making process. In this work, we aimed at evaluating several such importance estimators in a simple application, namely classifying computed tomography (CT) images acquired with or without contrast agent.\n",
      "\n",
      "In medical imaging, a contrast agent is a substance ingested by the patient or injected in order to generate contrast enhancement and visibility increase of anatomical structures or blood vessels [17]. In our chosen application of CT images of the lungs, an iodine-based solution is usually used and contrast-enhanced images can be quite easily visually identified based on the resulting higher contrast between blood vessels and surrounding tissues [3,8]. The extraction and exploitation of quantitative metrics from medical images for the purpose of building diagnostic and predictive models is called radiomics [13]. Radiomic features (shape descriptors, intensity metrics or textural features) have been shown to provide relevant information for diagnostic and prognostic tasks in various applications [13]. Over the last few years, deep learning techniques have become an important component of radiomics developments, including with end-to-end architectures [6], however the resulting models are notoriously less interpretable than models based on a combination of usual radiomic features [18].\n",
      "\n",
      "In the present work, the contrast agent detection/classification task was performed by a trained ResNet-50 architecture modified to accept gray scale CT scan slices as input, while the output layer consists of only one neuron, coding for both without or with contrast agent decisions, with a value respectively closer to 0 or 1. The dataset used for training and testing is a combination of the Lung Image Database Consortium image collection (LIDC-IDRI) [2] and A Lung Nodule Database (LNDb) [19]. In total, 1312 patients scans are available, with more than 320k slices. The model was trained using 2074 slices obtained by selecting 2 slices in each of the 1037 patient 3D scans. On a test set consisting of 4144 slices from 259 different patients, the model obtained 99.4% accuracy. We emphasize on the fact that the task was chosen to be easy and our focus was obviously not on the training of the model itself. Instead, the task was chosen because experts could quite easily visually identify the parts of the image (i.e., anatomical areas) that they think are relevant to identify whether a contrast agent is present (i.e., parts of the image they first look at when performing the task). This allowed us to explore how well the explanations provided by the importance estimators would match with this human expertise, as is explained in more details below. For the evaluation of importance estimators, 290 slices of the chest, paired to their respective labels, were sampled and the corresponding segmentation masks were produced with the help of a clinical expert.\n",
      "\n",
      "A popular approach to mitigate the challenge of interpretability in deep learning is to estimate the importance of input features (i.e., pixels) with respect to the model’s predictions. In the literature a wide variety of such importance estimators, also referred to as saliency maps, has been proposed, see for instance [23,24,26,27]. The introduction of new saliency map methods is often motivated by certain properties such as sparsity and absence of noise [16,24]. That such properties are seen as desirable is mainly motivated by our human expectation of what the model should be doing, whereas interpretability methods are supposed to explain what the model is doing. An underlying issue is that it is usually not possible to directly evaluate which of these approaches explains a given model most accurately because there is no ground truth available for the model behavior. If there was, it would not be necessary to estimate the importance of input features in the first place. Although a real ground truth is not achievable, a proxy gold standard was used here in the form of human-annotated masks (Fig. 1). These masks encode the expectation of what regions of the image the network should use to predict the presence or absence of contrast agent, at least if we assume that it should rely on the same information as the human expert.\n",
      "\n",
      "To evaluate importance estimators despite this lack of a ground truth, we considered three different metrics. The design of our computational experiments allowed us to explore and compare model-sensitive evaluation metrics and ones that focus on human expectation and intuition in the context of a medical application. First, in the perturbation-based evaluation, according to a given set of importance scores, we masked input pixels deemed the most important first (MiF) or the least important first (LiF), and measure the resulting accuracy decrease of the model [20,21]. The area between MiF and LiF curves was used as an evaluation metric called fidelity [5] (Fig. 2). Second, segmentation masks were compared against given importance scores on a pixel level. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) approximated how well the importance estimators matched human expectation about the importance of input pixels. Third, importance scores images were segmented to obtain region-wise maps based on the XRAI methodological framework [15] that can be partially occluded. Dice similarity coefficients (DSC) [7] were computed to assess the overlap of those segmented regions with the reference masks.\n",
      "\n",
      "![figure/14_0.png](figure/14_0.png)\n",
      "\n",
      "**Fig. 1.** Top row: Sample of a CT scan slice of chest without contrast agent, the expert-defined segmentation mask and a saliency map obtained using SmoothGrad Squared (from left to right). Bottom row: The same graphics as in the top row, here for a sample slice with contrast agent. Notice how areas such as the aorta and the heart regions are highlighted in this case.\n",
      "\n",
      "## 2 Importance Estimators\n",
      "To interpret classification of deep neural networks for CT scans, we employed a family of importance estimators, also known as saliency maps. We focused on pixel-level methods that quantify importance of input pixels with respect to output probabilities. The majority of applied methods are based on classical gradients (e.g., vanilla saliency maps) [4,23], except Deconvolution [27,28]. In the next section we briefly describe the importance estimators under evaluation.\n",
      "\n",
      "All estimators produce negative and positive importance scores, except for Squared SmoothGrad which produces only positive ones. Many studies routinely use the absolute values of importance scores in their applications, e.g. [23]. It is however a priori unclear to what degree the sign of importance scores is meaningful and contributes to explain the model. When evaluating the different importance estimators we therefore considered not only the original scores but also their absolute values. The following estimators were evaluated:\n",
      "\n",
      "- **Backpropagation** [4,23]: Gradients of the class score $S_c$ \\(1\\) with respect to input pixels $x_i$\n",
      "\n",
      "$$ e = \\frac{\\partial S_c}{\\partial x_i} $$\n",
      "\n",
      "- **Deconvolution** [27,28]: Implementation of a mirror version of the trained network, where:\n",
      "\n",
      "---\n",
      "1 The class score $S_c$ is the activation of the neuron in the prediction vector that corresponds to the class $c$.\n",
      "<NEWLINE>None- the optimized weights are transferred,\n",
      "- transposed convolutions are applied,\n",
      "- activation functions are applied on deconvoluted data,\n",
      "- pooled matrices are unpooled using maximum locations memorization.\n",
      "\n",
      "- **Integrated Gradients** [26]: Average over gradients obtained from inputs interpolated between a reference input $x'$ and $x$\n",
      "\n",
      "  $$\n",
      "  e = (x_i - x'_i) \\times \\sum_{k=1}^m \\frac{\\partial S_c \\left( x' + \\frac{k}{m} (x - x') \\right)}{\\partial x_i} \\times \\frac{1}{m},\n",
      "  $$\n",
      "\n",
      "  where $x'$ is chosen to be a black image and $m = 25$.\n",
      "\n",
      "- **Integrated Gradients (Black and White)** [15]: Variant of the original Integrated Gradients method, using both black and white images as reference. This modification theoretically enables importance of dark pixels when multiplying with the input image.\n",
      "\n",
      "- **Expected Gradients** [10]: Based on Integrated Gradients, the black reference is replaced with a set of training images, and the interpolation coefficients are randomly chosen\n",
      "\n",
      "  $$\n",
      "  e = \\mathbb{E}_{x' \\sim D,\\alpha \\sim U(0,1)} \\left[ (x_i - x'_i) \\times \\frac{\\partial S_c (x' + \\alpha \\times (x - x'))}{\\partial x_i} \\right]\n",
      "  $$\n",
      "\n",
      "- **SmoothGrad** [24]: Average over gradients obtained from inputs with added noise\n",
      "\n",
      "  $$\n",
      "  e = \\frac{1}{n} \\sum_{1}^n \\hat{e} \\left( x + \\mathcal{N}(0,\\sigma^2) \\right),\n",
      "  $$\n",
      "\n",
      "  where $\\mathcal{N}(0,\\sigma^2)$ represents Gaussian noise with standard deviation $\\sigma$, $\\hat{e}$ is obtained using Backpropagation and $n = 15$.\n",
      "\n",
      "- **Squared SmoothGrad** [14]: Variant of SmoothGrad that squares $\\hat{e}$ before averaging\n",
      "\n",
      "  $$\n",
      "  e = \\frac{1}{n} \\sum_{1}^n \\hat{e} \\left( x + \\mathcal{N}(0,\\sigma^2) \\right)^2.\n",
      "  $$\n",
      "\n",
      "Lastly, we created a random baseline, by drawing independent and identically distributed numbers from the uniform distribution as important scores in the same dimension as the input image. This random baseline was compared against the aforementioned estimators.\n",
      "\n",
      "Considering the affirmations made in Integrated and Expected Gradients papers [10,26] about the meaning of importance scores sign, we applied a targeted inversion of signs in the heatmaps of those saliency methods, to always have positive scores as important for the prediction made by the network. This also allows effective comparison with all other methods, without advanced processing.\n",
      "\n",
      "# Evaluation Methods\n",
      "\n",
      "In this section we describe the different metrics used for evaluating the importance estimators. The first method is model-sensitive and the two others are based on a comparison with the segmentation masks.\n",
      "\n",
      "## Model Accuracy per Input Feature Perturbation\n",
      "\n",
      "![](figure/16_0.png)\n",
      "\n",
      "**Fig. 2.** The perturbation-based evaluation metric, called _fidelity_, is defined as the area $F$ between the MiF and LiF curves. This does not consider manually annotated segmentation masks in evaluation of importance estimators.\n",
      "\n",
      "In order to measure how accurately a given importance estimator explains the model we employed a perturbation-based approach [20,21]. To this end the estimated importance scores were first ranked either MiF or LiF and then, according to this ranking, an increasing fraction of pixels, in 2.5% steps, was masked. To effectively remove information we chose a label-dependent perturbation, namely for samples with contrast agent pixels were masked with value 0 and in the other case they were masked with value 1. Thereby any information concerning the presence or absence of a contrast agent carried by these pixels was removed. The masked images were then fed to the model and the new accuracy was measured.\n",
      "As an evaluation metric, which we called _fidelity_, we defined the area $F$ between the perturbation curves (see Fig. 2), with larger $F$ indicating better performance [5]. An importance estimator is therefore considered to perform well if the model’s accuracy drops fast when masking MiF and if simultaneously the accuracy is maintained when masking LiF. With this choice we evaluated if the importance estimators were able to correctly identify both the most important and least important pixels.\n",
      "\n",
      "## Concordance Between Importance Scores and Segmentation\n",
      "\n",
      "Segmentation masks, which indicate the regions relevant for the classification, according to experts, were manually generated. Clinicians may expect that a reasonable classification algorithm for contrast agents would focus on those areas, and therefore these segmentation masks were used to evaluate how well importance estimators match the expert visual process for identifying the presence of contrast agent. We compared the outputs (e.g., importance scores) of interpretability methods against the segmentation masks, to get the receiver operating characteristic (ROC) curve [12]. More specifically, at an increasing threshold of (normalized) importance scores, we calculated the true positive rate (TPR) and the false positive rate (FPR) for each CT image. Segmentation masks, which were manually annotated by experts, are used such that if a pixel has an importance score above a threshold, it is considered a positive prediction and if that given pixel is within the segmented region, it is declared a true positive. The higher the true positive rate, the more sensitive the estimator is in terms of identifying pixels in the segmented region. We may plot ROC curves for all samples for which masks are provided. We then averaged TPR and FPR at each threshold to obtain the overall ROC for each method. The area under the curve (AUC) was approximated by the mean of the heights of available points. Greater upward deviation from the diagonal line in ROC and higher AUC values indicate better performance.\n",
      "\n",
      "## XRAI-Based Region-Wise Overlap Comparison\n",
      "\n",
      "![](figure/17_0.png)\n",
      "\n",
      "**Fig. 3.** _Top row_: The second slice example with contrast agents displayed in Fig. 1, its segmentation mask, the heatmap obtained from Expected Gradients, and the segmented version of the heatmap (from left to right). _Bottom row_: Displays of the most salient regions maps (between 1% and 5%).\n",
      "\n",
      "This metric is based on the XRAI approach [15]. In its original implementation, the XRAI process is composed of (1) segmentation of the input image using Felzenszwalb’s method [11], (2) computation of Integrated Gradients (Black and White version) importance scores on the input image, (3) ranking of the segmented regions of the image, according to their mean importance scores over the regions, (4) possibility to display any percentage of the most salient regions of the image. An example of this process’ output is shown in Fig. 3.\n",
      "This algorithm was initially considered as a method for generating saliency maps. We used it here for evaluation purposes by making two observations: (a) any importance estimator can be used instead of the originally proposed Integrated Gradients in the XRAI framework (the ones in our list are compatible); (b) once computed, the XRAI region-wise heatmaps for varying percentages can be directly compared against the available segmentation masks. From the described modifications, the new process involves variation of the saliency method before segmentation, and partial occlusion of segmented heatmap using a percentage threshold. Partial heatmaps are then evaluated using the Dice similarity coefficient (DSC) [7], describing the overlapping between two samples (i.e. images in this paper):\n",
      "\n",
      "$$\n",
      "DSC = \\frac{2 \\times |X \\cap Y|}{|X| + |Y|}.\n",
      "$$\n",
      "\n",
      "# Results\n",
      "\n",
      "## Model Accuracy per Input Feature Perturbation\n",
      "\n",
      "**Table 1.** Fidelity metric with and without application of absolute value in the post-processing of importance scores.\n",
      "\n",
      "| Estimator         | $F \\times 10$ (Original) | $F \\times 10$ (Absolute) |\n",
      "|-------------------|--------------------------|--------------------------|\n",
      "| SmoothGradSQ      | 6.2                      | 6.2                      |\n",
      "| SmoothGrad        | 3.4                      | 6.1                      |\n",
      "| IntGradBW         | 0.0                      | 5.7                      |\n",
      "| ExpectedGrad      | 3.6                      | 5.6                      |\n",
      "| Backpropagation   | 0.1                      | 4.9                      |\n",
      "| Deconvolution     | 2.6                      | 4.4                      |\n",
      "| IntGrad           | 0.0                      | 4.2                      |\n",
      "| Random            | 0.0                      | 0.0                      |\n",
      "\n",
      "The samples in the test set were masked according to the procedure described in Sect. 3.1 and then fed into the model to evaluate its prediction accuracy and obtain the perturbation curves. Since the test set was unbalanced the total model accuracy was obtained from averaging the prediction accuracies for each class.\n",
      "The results of this procedure, using fidelity as a metric, are summarized in Table 1. All methods clearly outperformed the random baseline when absolute\n",
      "\n",
      "![](figure/19_0.png)\n",
      "\n",
      "**Fig. 4.** Perturbation curves when pixels are masked MiF (_top row_) and LiF (_bottom row_). Notice how for the LiF curves the performance is much better when using absolute values. Overall fidelity metric, as calculated as the area between MiF and LiF curves, are shown for all estimators in Table 1.\n",
      "\n",
      "values were used, with Squared SmoothGrad performing best, closely followed by standard SmoothGrad. Every method led to better results when using the absolute values, with several methods scoring only zero fidelity when the original scores were used.\n",
      "To gain a better understanding of this behavior we plotted the perturbation curves in Fig. 4. Notice how for both versions of Integrated Gradients and Backpropagation the accuracy quickly dropped for LiF masking when the original importance scores were used, just as they did in the MiF case. In fact, all methods, with the exception of Squared SmoothGrad, showed an accelerated accuracy decrease when the original importance scores were used. This indicates that the pixels with negative importance score are actually evidence contributing to the correct prediction and are not counter-evidence, as one might expect.\n",
      "<NEWLINE>None"
     ]
    }
   ],
   "source": [
    "# 通过 ImageConverter 将图片转换为文本\n",
    "\n",
    "image_text = image_converter.convert(\n",
    "    images = test_surya_layout_pdf_data.images,\n",
    "    attached_images_map= test_surya_layout_pdf_data.attached_images_map,\n",
    "    work_dir=work_dir, # 图片在Markdown中的地址会相对于 workdir，默认是绝对路径\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save Lecture Notes in Artificial Intelligence\n",
    "with open(\"Lecture Notes in Artificial Intelligence.md\", \"w\") as f:\n",
    "    f.write(image_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "e2m",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
